Udacity - Data Scientist Nanodegree nd025 v1.0.0

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Udacity - Data Scientist Nanodegree nd025 v1.0.0

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收录时间：2021-03-07
最近下载：2025-01-01

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文件列表

  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4 42.7 MB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/02. What Do Data Scientists at AirBnB Do-q7sw9vc5o1U.mp4 42.1 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/24. Data Engineering-z6r2e_V0Td0.mp4 37.0 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Py Part 2 V1-u50_ZyKqt8g.mp4 36.3 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4 34.2 MB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.mp4 34.1 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4 33.2 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/05. Py Part 3 V2-u8hDj5aJK6I.mp4 29.7 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/06. L1 06 How To Succeed REPLACEMENT-JRnZOZR97QQ.mp4 28.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/08. L1 06 How To Succeed REPLACEMENT-JRnZOZR97QQ.mp4 28.4 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/07. Py Part 5 V2-coBbbrGZXI0.mp4 28.4 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 28.1 MB
  • Part 01-Module 04-Lesson 01_What Is Ahead/03. Rachel from Kaggle-uVsYYzxbyIg.mp4 27.7 MB
  • Part 15-Module 01-Lesson 06_Web Development/14. Bootstrap Library-KsrqjguHWUI.mp4 27.6 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4 27.6 MB
  • Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/03. Dan Frank Interview-Me-KRvZW1QQ.mp4 27.4 MB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/04. DSND T2 Intro Dan Frank V4-rTCPmVQDsEw.mp4 27.1 MB
  • Part 05-Module 01-Lesson 01_Congratulations!/04. Arvato Final Project-qBR6A0IQXEE.mp4 26.6 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Arvato Final Project-qBR6A0IQXEE.mp4 26.6 MB
  • Part 18-Module 01-Lesson 01_Data Scientist Capstone/06. Arvato Final Project-qBR6A0IQXEE.mp4 26.6 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Introduction to Blogging for Data Science-WrvGpRN5XQI.mp4 26.5 MB
  • Part 05-Module 01-Lesson 01_Congratulations!/04. Introduction to Blogging for Data Science-WrvGpRN5XQI.mp4 26.5 MB
  • Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/01. Blogging for Data Science-WrvGpRN5XQI.mp4 26.5 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.mp4 26.3 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/10. Py Part 8 V1-3eqn5sgCOsY.mp4 26.1 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/13. How to Break Into the Field Solution-Db_2Lmwo4EY.mp4 25.7 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.mp4 24.9 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4 24.5 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4 24.3 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/29. Model Diagnostics In Python-1Z4eorbfOOc.mp4 23.9 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4 23.6 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.mp4 23.1 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.mp4 23.0 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/40. Categorical Variables-p3gDUkBD9uM.mp4 22.9 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/05. Interview with Art - Part 1-ClLYamtaO-Q.mp4 22.8 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 22.7 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.mp4 22.7 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 22.1 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.mp4 22.0 MB
  • Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/09. Linear Transformations 1-99jYIxBRDww.mp4 21.8 MB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.mp4 21.6 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/33. Data Engineering Importance-VO-OrJ0JqxM.mp4 21.6 MB
  • Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.mp4 21.5 MB
  • Part 04-Module 01-Lesson 04_PCA/12. 11 PCA 1 Solution V1-u0rJRmubQ44.mp4 21.2 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 03362 V1-MwRSg5RASoc.mp4 21.1 MB
  • Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/11. Linear Transformations 3-g_yTyRwMzXU.mp4 21.1 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 20.9 MB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.mp4 20.9 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.mp4 20.7 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/04. Cleaning-qawXp9DPV6I.mp4 20.5 MB
  • Part 15-Module 01-Lesson 06_Web Development/30. Deployment-YPfNzpnm_Rk.mp4 20.3 MB
  • Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.mp4 19.9 MB
  • Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/04. Experimental Design Insights With Richard Sharp-XDBw2nfOrsU.mp4 19.8 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 1051320 V1-_4N6h82szWo.mp4 19.8 MB
  • Part 01-Module 04-Lesson 01_What Is Ahead/02. Adam from IBM-NjjtY5UHyac.mp4 19.8 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 19.7 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 19.5 MB
  • Part 06-Module 01-Lesson 03_Control Flow/10. For Loops-UtX0PXSUCdY.mp4 19.3 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/05. How Does MLR Work-bvM6eUYyurA.mp4 19.3 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/35. Imputation Methods-OwEWSBitF-Q.mp4 19.3 MB
  • Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.mp4 19.2 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 19.0 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 19.0 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.mp4 18.9 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 11123244 V1-QlILlYuWF9U.mp4 18.8 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.mp4 18.6 MB
  • Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/08. Advanced API Code Walk-through-AkqO534YooE.mp4 18.6 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 18.6 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/32. Removing Data Part II-lPl6-Z098Rs.mp4 18.5 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/17. Multicollinearity VIFs-wbtrXMusDe8.mp4 18.4 MB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.mp4 18.2 MB
  • Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.mp4 18.2 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/21. Predicting Salary-HTp4LA1MJh8.mp4 18.2 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/02. Meet The Instructors-XAU2Nf51vfU.mp4 18.2 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/22. Interactions Higher Order Terms-gMHwogzqPOk.mp4 18.1 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Subquery Solution Video-Y6S3S0LsMrw.mp4 18.1 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.mp4 18.1 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/13. Strings-ySZDrs-nNqg.mp4 18.1 MB
  • Part 01-Module 04-Lesson 01_What Is Ahead/04. What'S Ahead Figure 8 Fix-SE4TQnOwmBI.mp4 17.9 MB
  • Part 15-Module 01-Lesson 06_Web Development/27. 40 Screencast Flask Pandas Plotly Part4 V2-4IF2G9Fehb4.mp4 17.9 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 17.9 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 01725 V1-UFmfDAiaOmw.mp4 17.8 MB
  • Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.mp4 17.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.mp4 17.7 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.mp4 17.6 MB
  • Part 15-Module 01-Lesson 01_Introduction to Software Engineering/04. L1 04 Meet Juno V1 V2-c4r2nGMogfM.mp4 17.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.mp4 17.2 MB
  • Part 04-Module 01-Lesson 06_Project Identify Customer Segments/01. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.mp4 17.2 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/07. A Look at the Data-vPHVUYvCNGE.mp4 17.2 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/27. 20 Putting Code On PyPi V1-4uosDOKn5LI.mp4 17.1 MB
  • Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks/01. Starbucks Lab-QPKRboscAf4.mp4 17.0 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/08. Dummy Variables--QTgDd-fZuA.mp4 17.0 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/43. Putting It All Together-3SX4dMZPNEI.mp4 16.8 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.mp4 16.7 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/08. Py Part 6 V1-HiTih59dCWQ.mp4 16.7 MB
  • Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.mp4 16.7 MB
  • Part 15-Module 01-Lesson 06_Web Development/10. CSS-s_sdzHR9cs0.mp4 16.7 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.mp4 16.6 MB
  • Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.mp4 16.5 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/17. Job Satisfaction-OjCNMhWlYh8.mp4 16.2 MB
  • Part 06-Module 01-Lesson 04_Functions/02. Defining Functions-IP_tJYhynbc.mp4 16.2 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 16.2 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/08. Números inteiros e floats-MiJ1vfWp-Ts.mp4 16.2 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.mp4 16.1 MB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.mp4 15.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.mp4 15.7 MB
  • Part 01-Module 04-Lesson 01_What Is Ahead/01. What Do Data Scientists Do-sN2DbIJUZmw.mp4 15.7 MB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/01. What Do Data Scientists Do-sN2DbIJUZmw.mp4 15.7 MB
  • Part 15-Module 01-Lesson 06_Web Development/16. 18 Screencast Plotly V2-QsmOW1jNeio.mp4 15.5 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/09. PyTorch - Part 7-hFu7GTfRWks.mp4 15.3 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.mp4 15.3 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.mp4 15.1 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.mp4 15.1 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 15.1 MB
  • Part 06-Module 01-Lesson 06_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.mp4 14.9 MB
  • Part 11-Module 01-Lesson 03_Linear Combination/02. Linear Combinations 2-RsKJNDTb8nw.mp4 14.9 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/03. Part 1 V2-n4mbZYIfKb4.mp4 14.5 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/02. Multiple Linear Regression-rvYZp99nj6c.mp4 14.5 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Figure 8 Project-QbLVh5GTuJQ.mp4 14.3 MB
  • Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/01. Figure 8 Project-QbLVh5GTuJQ.mp4 14.3 MB
  • Part 05-Module 01-Lesson 01_Congratulations!/04. Figure 8 Project-QbLVh5GTuJQ.mp4 14.3 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Build A Recommendation Engine IBM-A0rVwTbntf4.mp4 14.2 MB
  • Part 05-Module 01-Lesson 01_Congratulations!/04. Build A Recommendation Engine IBM-A0rVwTbntf4.mp4 14.2 MB
  • Part 17-Module 04-Lesson 01_Recommendation Engines/01. IBM Project Overview-XP_f64c07Gc.mp4 14.2 MB
  • Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/10. Linear Transformations 2-imtEd8M6__s.mp4 14.1 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/11. Dummy Variable Interpretation-TxP_TD0kbOo.mp4 14.0 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 06 Lists Methods V1-tz2Ja1Eaeqo.mp4 14.0 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 14.0 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.mp4 13.9 MB
  • Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.mp4 13.9 MB
  • Part 06-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.mp4 13.9 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.8 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/37. Imputing Values-nTM4HiDneeE.mp4 13.8 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/15. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.8 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 11442204 V1-8kdRNQnqSGA.mp4 13.7 MB
  • Part 05-Module 01-Lesson 01_Congratulations!/03. Next Steps-kXMCKZ4HqsM.mp4 13.6 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 13.6 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 13.6 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.mp4 13.5 MB
  • Part 04-Module 01-Lesson 04_PCA/10. 09 PCA V1-0RLDZWeq5JE.mp4 13.4 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 18003113 V1-2M-WX2X2ts4.mp4 13.4 MB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.mp4 13.4 MB
  • Part 16-Module 01-Lesson 01_Introduction to Data Engineering/03. Figure 8 Project V2-adtlHL42AuQ.mp4 13.2 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 13.2 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 33514421 V1-TCaeEdrbYRc.mp4 13.2 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 13.2 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.mp4 13.2 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.mp4 13.1 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.9 MB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/01. A Repository's History - Intro-UBmg3syQS0E.mp4 12.9 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/23. What Happened-gLn6_Z3nwcc.mp4 12.7 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 12.6 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.mp4 12.5 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.mp4 12.5 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.mp4 12.4 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4 12.3 MB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.mp4 12.1 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.mp4 12.1 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.mp4 12.0 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.mp4 12.0 MB
  • Part 06-Module 01-Lesson 05_Scripting/20. Importing Files-qjeSn6zZbR0.mp4 12.0 MB
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.mp4 11.9 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/18. Multicollinearity VIFs-uiF3UcDWwPI.mp4 11.9 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.mp4 11.8 MB
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.mp4 11.8 MB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/03. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.8 MB
  • Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.mp4 11.8 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.mp4 11.7 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.mp4 11.6 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.mp4 11.6 MB
  • Part 15-Module 01-Lesson 01_Introduction to Software Engineering/01. Introduction To Software Engineering-7kphieW4yl4.mp4 11.5 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.mp4 11.5 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.mp4 11.5 MB
  • Part 06-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.mp4 11.4 MB
  • Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.mp4 11.3 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.mp4 11.3 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.mp4 11.3 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/01. Shell Intro--EtN5oD8MM0.mp4 11.3 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/09. SQL Subquery Video-10pmKmTI_CA.mp4 11.3 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.mp4 11.3 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.mp4 11.2 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.mp4 11.2 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/14. 14 Funk SVD-H8gdwXy_npI.mp4 11.2 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 4321430 V1-zVGhBQNgbc4.mp4 11.1 MB
  • Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.mp4 11.1 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/24. Recommendations 2 25 V1-zgz5WYlI5fE.mp4 11.0 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.mp4 10.9 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.mp4 10.8 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 14502145 V1-cvQngTUOWbM.mp4 10.8 MB
  • Part 15-Module 01-Lesson 06_Web Development/05. 6 Screencast HTML Code V2-G7fBus1JSc0.mp4 10.8 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.8 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4 10.8 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.mp4 10.7 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/14. Bootcamps-l2tYmee3kxo.mp4 10.7 MB
  • Part 02-Module 01-Lesson 10_Finding Donors Project/06. Kaggle Project Final For Classroom-Ssttix340C8.mp4 10.6 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Kaggle Project Final For Classroom-Ssttix340C8.mp4 10.6 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.mp4 10.6 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/10. Meet the Careers Team-cuKecPpZ7PM.mp4 10.6 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/07. Meet the Careers Team-cuKecPpZ7PM.mp4 10.6 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.mp4 10.6 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.mp4 10.6 MB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.mp4 10.5 MB
  • Part 04-Module 01-Lesson 05_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 10.4 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.mp4 10.4 MB
  • Part 04-Module 01-Lesson 01_Clustering/11. 12 KMeans In Scikit Learn Solution V1-IIVsWFq2DXk.mp4 10.3 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/02. L2 02 Clean Mod Code Vid 1 V1 V2-RjHV8kRpVbA.mp4 10.3 MB
  • Part 06-Module 01-Lesson 06_NumPy/08. NumPy 4 V1-jeU7lLgyMms.mp4 10.3 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/09. SVD-t2XTuHq6-xc.mp4 10.3 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/10. Data Ink Ratio-gW2FapuYV4A.mp4 10.3 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/02. Fitting Logistic Regression-Dg0rBDQnIYg.mp4 10.2 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/04. Fitting Logistic Regression In Python-baQf-XiZQQ4.mp4 10.2 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 10.2 MB
  • Part 15-Module 01-Lesson 06_Web Development/12. 14 Screencast JavaScript V2-vgXUKgsT_48.mp4 10.1 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.mp4 10.1 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/26. Scikitlearn Source Code-4_qkqMsbthg.mp4 10.1 MB
  • Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.mp4 10.1 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.mp4 10.1 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.mp4 9.9 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.8 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/05. Using Workspaces-45N9NK6kQ0Y.mp4 9.8 MB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.mp4 9.7 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4 9.7 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.mp4 9.7 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/03. World Bank Datasets-lNPzOLzZVbw.mp4 9.7 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.mp4 9.7 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.mp4 9.7 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.mp4 9.7 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.7 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 10131720 V1-DWHYK0XSI70.mp4 9.7 MB
  • Part 04-Module 01-Lesson 05_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.6 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/03. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.6 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.6 MB
  • Part 04-Module 01-Lesson 04_PCA/15. 14 Interpretation Solution V1-wU2duZa0ds0.mp4 9.6 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.mp4 9.6 MB
  • Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.mp4 9.6 MB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/01. Welcome To DSND T2 V1 1 V1-ebJZrc2y85Q.mp4 9.5 MB
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.mp4 9.5 MB
  • Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.mp4 9.5 MB
  • Part 04-Module 01-Lesson 01_Clustering/09. 10 KMeans In Scikit Learn V1-jkEgQLOcCGo.mp4 9.4 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 36044330 V1-b5gFe8Ij-g0.mp4 9.4 MB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 9.4 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.mp4 9.4 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 01 Welcome-lbR82UD5F0c.mp4 9.3 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 0950 V1-yrNZ0sQwNcs.mp4 9.3 MB
  • Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.mp4 9.3 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 012725 V1-Y1dN-mB39rM.mp4 9.2 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/26. Regularization-PyFNIcsNma0.mp4 9.2 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 9.1 MB
  • Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/01. C4 Intro-gXlqR86h0yI.mp4 9.1 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 20332637 V1-UnDocJ9VUec.mp4 9.1 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 10491855 V2-pjoxB00grHw.mp4 9.0 MB
  • Part 11-Module 01-Lesson 01_Introduction/03. Essence Of Linear Algebra Intro -EHcxDZpeGFg.mp4 9.0 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.mp4 9.0 MB
  • Part 06-Module 01-Lesson 06_NumPy/07. NumPy 3 V1-Rt4aydeo9F8.mp4 8.9 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/11. How To Break Into The Field-0-Y39LZ80VE.mp4 8.9 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.9 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/14. Using Color-6bAedqD3ilw.mp4 8.9 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 4381128 V1-B6bELCg6gMs.mp4 8.9 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/33. Imputing Missing Values-CEWIPjz_gCE.mp4 8.8 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/09. L2 06 Efficient Code V1 V2-LbtxY7xetBw.mp4 8.8 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4 8.8 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4 8.8 MB
  • Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.mp4 8.8 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 4422330 V1-DJfwhP_vvh4.mp4 8.8 MB
  • Part 16-Module 01-Lesson 01_Introduction to Data Engineering/01. 01 Welcome V1 V2-Ykd7CN5dDx0.mp4 8.8 MB
  • Part 10-Module 01-Lesson 07_Working With Remotes/01. Intro-SBUOhyXcR1Q.mp4 8.7 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.mp4 8.7 MB
  • Part 11-Module 01-Lesson 03_Linear Combination/01. Linear Combinations 1-fmal7UE7dEE.mp4 8.7 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/04. Object Oriented Programming Syntax-Y8ZVw1LHI8E.mp4 8.7 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.mp4 8.7 MB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/04. Exploratory vs. Explanatory Analysis-wvgBSMks4p8.mp4 8.6 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.mp4 8.6 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/04. 01 Writing Clean Code V1-wNaiahWCwkQ.mp4 8.6 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Action-cL6ehKtJLUM.mp4 8.6 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 10491855 V1-BafXxtTuZgQ.mp4 8.5 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/10. 03 Optimizing Common Books V1-WF9n_19V08g.mp4 8.5 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/29. Outliers How To Find Them-ksqzOCSAp5U.mp4 8.5 MB
  • Part 06-Module 01-Lesson 07_Pandas/12. Pandas 7 V1-ruTYp-twXO0.mp4 8.5 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.5 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/10. How The Gaussian Class Works-N-5I0d1zJHI.mp4 8.5 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/38. Bloopers Intro 1 V1-Y1weHponR2Q.mp4 8.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.mp4 8.4 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.4 MB
  • Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.mp4 8.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/18. Matching Encodings-398xRMnhjGk.mp4 8.4 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 8.4 MB
  • Part 04-Module 01-Lesson 05_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 8.3 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17b 15022032 V1-N9ytffw5AMg.mp4 8.3 MB
  • Part 15-Module 01-Lesson 06_Web Development/25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.mp4 8.3 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.mp4 8.3 MB
  • Part 06-Module 01-Lesson 07_Pandas/10. Pandas 6 V1-GS1kj04XQcM.mp4 8.3 MB
  • Part 06-Module 01-Lesson 07_Pandas/09. Pandas 5 V1-lClsJnZn_7w.mp4 8.2 MB
  • Part 04-Module 01-Lesson 01_Clustering/20. 19 Feature Scaling Solution V1-xddMZP2SQ1U.mp4 8.2 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt3-_HTolKktaC4.mp4 8.2 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 23313044 V1-pcaaBWbe34Y.mp4 8.2 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.mp4 8.1 MB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-resolve-merge-conflict.gif 8.1 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.mp4 8.1 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.mp4 8.0 MB
  • Part 06-Module 01-Lesson 03_Control Flow/02. Indentation-G8qUNOTHtrM.mp4 8.0 MB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. L2 - Pushing To A Fork-WRgNpr19t48.mp4 8.0 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/06. 02 Writing Modular Code V2-qN6EOyNlSnk.mp4 8.0 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.mp4 8.0 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.mp4 8.0 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4 7.9 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/07. Regularization-ndYnUrx8xvs.mp4 7.9 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/03. Types Of Experiments-7ihDj4M7EiU.mp4 7.9 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.mp4 7.9 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/21. 15 Making a Package v2-Hj2OBr1CGZM.mp4 7.9 MB
  • Part 06-Module 01-Lesson 06_NumPy/04. NumPy 1 V1-EOHW29kDg7w.mp4 7.9 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.mp4 7.9 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/29. 11 CASE V2-BInXuTY_FzE.mp4 7.9 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/14. Other JOINs-4edRxFmWUEw.mp4 7.9 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.mp4 7.8 MB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.mp4 7.8 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.mp4 7.8 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4 7.8 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.mp4 7.8 MB
  • Part 06-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.mp4 7.8 MB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/09. Programming Environment Setup-EKxDnCK0NAk.mp4 7.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.mp4 7.7 MB
  • Part 06-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.mp4 7.7 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/30. Removing Data-97UTBiybYTs.mp4 7.7 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.mp4 7.7 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.mp4 7.6 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/19. What Is A P-value Anyway-eU6pUZjqviA.mp4 7.6 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17b 5451216 V1-lf2Q0AE5esk.mp4 7.6 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.6 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.mp4 7.6 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.mp4 7.5 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 23242831 V1-WqNi0B_oRuA.mp4 7.5 MB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.mp4 7.5 MB
  • Part 15-Module 01-Lesson 06_Web Development/20. 22 Screencast Flask V2-i_U3O-7cymk.mp4 7.5 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.mp4 7.4 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.4 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/03. L2 031 Levels Of Measurement And Types Of Data V6-3Plhn5Q4xIA.mp4 7.4 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.4 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 2 Advantages Of Using Pipelines V1 V2-eT1MS3n8fZ8.mp4 7.4 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 4271048 V1-2On65U7Panw.mp4 7.4 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.mp4 7.3 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.mp4 7.3 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. DataVis L3 03 V2-srRhFrSPdvs.mp4 7.3 MB
  • Part 06-Module 01-Lesson 07_Pandas/08. Pandas 4 V1-eMHUn9v9dds.mp4 7.3 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 7.3 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/10. More Personalized Recommendations-9l8mi7i6iW4.mp4 7.2 MB
  • Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.mp4 7.1 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. DataVis L5C02 V3-bgDNMfG9Gfs.mp4 7.1 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/09. Business And Data Understanding - Part 2-iInjuIgBWIo.mp4 7.1 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.mp4 7.1 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment Pt 2-PYzN1usi7QY.mp4 7.1 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.mp4 7.0 MB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.mp4 7.0 MB
  • Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.mp4 7.0 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.mp4 7.0 MB
  • Part 15-Module 01-Lesson 06_Web Development/24. Flask Pandas Plotly Part 1-xg7P8MnItdI.mp4 7.0 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/15. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
  • Part 18-Module 01-Lesson 01_Data Scientist Capstone/09. Capstone-bq-H7M5BU3U.mp4 7.0 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.mp4 6.9 MB
  • Part 06-Module 01-Lesson 06_NumPy/11. NumPy 6 V1-wtLRuGK0kW4.mp4 6.9 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.9 MB
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  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.mp4 6.9 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.mp4 6.9 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.9 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/18. It Is Not Always About ML-ECqflypBU7M.mp4 6.9 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.mp4 6.9 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.mp4 6.9 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.mp4 6.8 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/11. Design Integrity-y72_fVFtqlY.mp4 6.8 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 31423505 V1-A0uOjClDnW8.mp4 6.8 MB
  • Part 02-Module 01-Lesson 10_Finding Donors Project/07. Project 1-PNsxDWtpQTk.mp4 6.7 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4 6.7 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.mp4 6.7 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. L3 031 Bar Charts V3-ybXcduB6cXA.mp4 6.7 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.mp4 6.7 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/01. Introduction-RVcFzwBXI2M.mp4 6.7 MB
  • Part 19-Module 01-Lesson 01_Congratulations!/01. Congrats-OTp4YOTDd0Q.mp4 6.7 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 0422 V1-J4MOXJhMGGA.mp4 6.7 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.mp4 6.6 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.6 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.mp4 6.6 MB
  • Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.mp4 6.6 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.mp4 6.6 MB
  • Part 11-Module 01-Lesson 02_Vectors/03. Vectors 3-mWV_MpEjz9c.mp4 6.6 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.mp4 6.6 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/06. Notes On OOP-NcgDIWm6iBA.mp4 6.6 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.mp4 6.6 MB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/01. Introduction-Yg0gBpTzkMo.mp4 6.5 MB
  • Part 15-Module 01-Lesson 06_Web Development/22. Flask and Pandas-L_M_8UVY42k.mp4 6.5 MB
  • Part 18-Module 01-Lesson 01_Data Scientist Capstone/01. Capstone-jewlarqqbTo.mp4 6.5 MB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.mp4 6.5 MB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/08. Data Vis L6 C06 V1-qIot9qrvcF8.mp4 6.5 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4 6.5 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.5 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.5 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/29. L3 21 Outro v1 V2-DStO1hBKtHQ.mp4 6.5 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/02. What Makes a Bad Visual-zbvB_9f7bFs.mp4 6.4 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.4 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.mp4 6.4 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/05. Experiment Size-sImRm8e01jA.mp4 6.4 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.mp4 6.4 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. L3 071 Pie Charts V3-kSrJGJHTKV8.mp4 6.3 MB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.mp4 6.3 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/13. 08 F1 Score SC V1-TRzBeL07fSg.mp4 6.3 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/26. Removing Data - When Is It OK-oQhIPq5AccU.mp4 6.3 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/09. Gaussian Class-TVzNdFYyJIU.mp4 6.3 MB
  • Part 04-Module 01-Lesson 05_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4 6.3 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.3 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.mp4 6.3 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/07. Using Dummy Tests-rURTLjh3Hlc.mp4 6.3 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.mp4 6.3 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/16. Identifying Recommendations-P60qvS_OTMg.mp4 6.2 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/41. Putting It All Together-PHaSifd-Mas.mp4 6.2 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.mp4 6.2 MB
  • Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Part II-1GRv1S6K8gQ.mp4 6.2 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/16. Shape, Size, and other Tools-fzEliHW3ZLM.mp4 6.2 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/07. L2 2 09 Test Driven Development DS V1 V2-M-eskssLcQM.mp4 6.2 MB
  • Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.mp4 6.2 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/18. JOINs and Filtering-aI1kbDDNs4w.mp4 6.2 MB
  • Part 15-Module 01-Lesson 06_Web Development/01. L4 Intro V2--PGMIIXFCgg.mp4 6.2 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.mp4 6.2 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4 6.2 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4 6.1 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/09. Checking Bias-ppjNNY4DhPw.mp4 6.1 MB
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.mp4 6.1 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4 6.1 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/03. The Data Science Process Business And Data Understanding-eG_jKQezhc4.mp4 6.1 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/26. Types Of Ratings-fMjqe4sxBlQ.mp4 6.0 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/19. 15 Pipelines And Grid Search V1 V3-HZaOiSxJjCY.mp4 6.0 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.mp4 6.0 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/13. GROUP BY-9vb67TF4WV0.mp4 6.0 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 26423140 V1-uNQHtPrfi4o.mp4 6.0 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/08. Using Pipelines-mxFrS8qpZ6Y.mp4 6.0 MB
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.mp4 6.0 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/20. The Cold Start Problem-DNz7aywJVzA.mp4 6.0 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 09 Lists And Membership Operators V2-rNV_E50wcWM.mp4 5.9 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/20. Notation for Random Variables-8NxTW1u4s-Y.mp4 5.9 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/01. L2 01 Intro V1 V1-z7v7oa--W48.mp4 5.9 MB
  • Part 06-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.mp4 5.9 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/03. Testing-gmxGRJSKEb0.mp4 5.9 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/15. LIMIT Statement-cCPHNNhBgpQ.mp4 5.9 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/01. 01 Intro V1 2 V4-iW4uqhfRk10.mp4 5.9 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/06. Why SVD-WdW1-rRQrLk.mp4 5.9 MB
  • Part 06-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.mp4 5.9 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/28. T-SNE-xxcK8oZ6_WE.mp4 5.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.mp4 5.8 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. DataVis L5C08 V2-fq-hakwfpZw.mp4 5.8 MB
  • Part 01-Module 04-Lesson 01_What Is Ahead/05. Outro-xj70jX9Moxs.mp4 5.8 MB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/06. Outro-xj70jX9Moxs.mp4 5.8 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 5.8 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/01. Intro-EBGMcpWe8-U.mp4 5.8 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/06. Creating Metrics-__7tzDUY870.mp4 5.8 MB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/06. L1 061 Visualization In Python V1-MFS-1veFC_c.mp4 5.8 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/04. Business And Data Understanding - Example-bXQTGS61BU8.mp4 5.8 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/02. History - Statisticians Perspective-zNNouqLGF9E.mp4 5.8 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 0425 V1-vPpX7ITgb3g.mp4 5.8 MB
  • Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.mp4 5.8 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.mp4 5.7 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/31. 38 Outliers What To Do With Them V1 V2-Yd_fPCmGNZ0.mp4 5.7 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4 5.7 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.7 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.7 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.7 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 1 Advantages Of Using Pipeline V1 V2-ASYcx911E2Q.mp4 5.7 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/15. L4 151 Lesson Summary V1-5igqM44KEmw.mp4 5.6 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.mp4 5.6 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.6 MB
  • Part 12-Module 01-Lesson 14_Regression/13. Fitting A Regression Line-xQob80zrT3s.mp4 5.6 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/23. Putting It All Together-r5jfD2uKnbQ.mp4 5.6 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.mp4 5.6 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.mp4 5.6 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/01. Introduction-5DfFaAl1Wmc.mp4 5.6 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/05. Extract Walk Through-Bbj8rQRRVoM.mp4 5.6 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.6 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.6 MB
  • Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/02. Disaster Relief Project Preview-DuwYAjqGM3E.mp4 5.6 MB
  • Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules-jPGyFgcIvsM.mp4 5.6 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.mp4 5.6 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.mp4 5.6 MB
  • Part 15-Module 01-Lesson 06_Web Development/18. L4 The Back End V2-Esl0NL63S2c.mp4 5.5 MB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.mp4 5.5 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.mp4 5.5 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. L4 021 Scatterplots And Correlation V2-wqMwTDVT9_Y.mp4 5.5 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.mp4 5.5 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/14. 22 Cleaning Data V1 V3-zYxgkUqTX0Y.mp4 5.5 MB
  • Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.mp4 5.5 MB
  • Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.mp4 5.5 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.mp4 5.5 MB
  • Part 04-Module 01-Lesson 04_PCA/08. PCA Properties-1oaaq-0wdB0.mp4 5.5 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.mp4 5.5 MB
  • Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.mp4 5.5 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4 5.5 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.mp4 5.5 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes Pt 2-yLdXcRXcfPw.mp4 5.4 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/13. Early Stopping-taIJZMNwRsI.mp4 5.4 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.4 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. DataVis L3 08 V2-f1we_0dUSXg.mp4 5.4 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.mp4 5.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/25. Duplicate Data-49ZwWRviAFg.mp4 5.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/36. 44 Feature Engineering V1 V1-7Bof5l8xjz8.mp4 5.4 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.4 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/12. Conclusions-yMRRXDKb428.mp4 5.4 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.mp4 5.4 MB
  • Part 06-Module 01-Lesson 06_NumPy/09. NumPy 5 V1-vGjI-WTnEbY.mp4 5.3 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.mp4 5.3 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/25. Removing Data - Why Not-w3-5Z5mEzTM.mp4 5.3 MB
  • Part 04-Module 01-Lesson 04_PCA/13. 12 Interpret PCA Results V1-ZX6EACfsZbc.mp4 5.3 MB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/01. L1 011 Data Visualization In Data Analysis Intro V3 V3-U1VapEELBfw.mp4 5.3 MB
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.mp4 5.3 MB
  • Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.mp4 5.2 MB
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  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/03. How Do We Know Our Recs Are Good-D0H_fjJ35CU.mp4 5.2 MB
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  • Part 02-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 5.0 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/23. Types Of Recommendations-uoXF81AO21E.mp4 5.0 MB
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  • Part 04-Module 01-Lesson 04_PCA/01. Introduction-tpFPcxoGxaE.mp4 4.9 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/20. Content Based Recommendations-pnGHpB77Mys.mp4 4.9 MB
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  • Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.mp4 4.9 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.mp4 4.9 MB
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  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/08. Checking Validity-H3H1SZXqDmQ.mp4 4.9 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/27. Removing Data - Other Considerations-xrXk_Tvi0oQ.mp4 4.9 MB
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  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.mp4 4.8 MB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/26. Recap-VM6GGNC2q8I.mp4 4.8 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Action-d3AGtKmHxUk.mp4 4.8 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/26. Conclusion-R5-OYqKk9Ys.mp4 4.8 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/26. DATE Functions Part II-UPWkDhW4cLI.mp4 4.8 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/13. Using Feature Unions-QmE6CMGar1U.mp4 4.8 MB
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  • Part 16-Module 01-Lesson 02_ETL Pipelines/12. Combining Data From Different Sources-IfMydJvU37M.mp4 4.8 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.mp4 4.7 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/17. Other Important Information-LF-CWF-1mX4.mp4 4.7 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.mp4 4.7 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.mp4 4.7 MB
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.mp4 4.7 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. L5 031 Color Palettes V1-nirOTWkuiSM.mp4 4.7 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/19. Organizing Code Into Modules-AARS10U5bbo.mp4 4.7 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/04. 06 Unit Tests V1-wb9jggHEvgI.mp4 4.7 MB
  • Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.mp4 4.7 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.mp4 4.7 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/img/business-money-pink-coins.jpg 4.7 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.mp4 4.7 MB
  • Part 15-Module 01-Lesson 06_Web Development/08. IDs and Classes-jnfDqdxDbO4.mp4 4.6 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.mp4 4.6 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/13. L3 10 Captivate Your Audience Now What V1-Iy08sZYuqkI.mp4 4.6 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/11. 19 Transform Intro V2 V3-SXp4Qa-rQJg.mp4 4.6 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.mp4 4.6 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing Introduction-mRbeT2XVL9w.mp4 4.6 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4 4.6 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.mp4 4.6 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.mp4 4.6 MB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/07. A Repository's History - Outro-9rUf2HbdAd8.mp4 4.6 MB
  • Part 04-Module 01-Lesson 01_Clustering/13. 14 How Does KMeans Work V1-y7yZyyHgyYU.mp4 4.6 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/15. L2 10 Documentation V1 V3-M45B2VbPgjo.mp4 4.6 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. L5 021 Non Positional Encodings For Third Variables V1-D91mm-qaDkk.mp4 4.6 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.mp4 4.6 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.mp4 4.6 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. Magic Methods in Code-oDuXThOqans.mp4 4.6 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.mp4 4.6 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/12. L5 121 Lesson Summary V1-SOBCduyymkQ.mp4 4.6 MB
  • Part 06-Module 01-Lesson 06_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.mp4 4.6 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4 4.5 MB
  • Part 04-Module 01-Lesson 01_Clustering/08. Elbow Method For Finding K-e7fqXpo63n8.mp4 4.5 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Your First Subquery-cTM1jPYXLoQ.mp4 4.5 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-xTamXY6Z9Kg.mp4 4.5 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. DataVis L3 04 V2-HLum_ys7RJ0.mp4 4.5 MB
  • Part 04-Module 01-Lesson 01_Clustering/15. Is That The Optimal Solution-g5aPtCpBNmw.mp4 4.5 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.mp4 4.5 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/09. What's Ahead-2Hxy2Jlu8nk.mp4 4.5 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.5 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. L3 02 Proced Vs OOP V1 V3-psXD_J8FnCQ.mp4 4.5 MB
  • Part 04-Module 01-Lesson 01_Clustering/01. Introduction-k7YOVTkFRJM.mp4 4.5 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/20. Precision and Recall-3vT0kSBCLdU.mp4 4.5 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.mp4 4.5 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/09. Gradient Descent-4s4x9h6AN5Y.mp4 4.5 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/01. Introduction-TRw4bvZuEG8.mp4 4.5 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. Performance Tuning 1-5mVfYZ_bfRo.mp4 4.5 MB
  • Part 06-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.mp4 4.4 MB
  • Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.mp4 4.4 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.mp4 4.4 MB
  • Part 04-Module 01-Lesson 01_Clustering/16. Feature Scaling-rpTVp7C8AXo.mp4 4.4 MB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.mp4 4.4 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.4 MB
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  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. DataVis L5C03 V2-iokI7HrxeNc.mp4 4.4 MB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/02. Cleaning with String Functions-y1fduSu7Ovc.mp4 4.4 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.mp4 4.4 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.mp4 4.4 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4 4.4 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.mp4 4.4 MB
  • Part 12-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.mp4 4.4 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.mp4 4.4 MB
  • Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.mp4 4.4 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.4 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.mp4 4.4 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-8QEAYYIyopY.mp4 4.4 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/01. L5 011 Intro V3-ckylQMBXB10.mp4 4.4 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.mp4 4.4 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.mp4 4.4 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. L4 121 Adaptations Of Univariate Plots V3-MXcqplnUB0o.mp4 4.4 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.4 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.mp4 4.4 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.3 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.3 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/41. How To Fix This-IPQZ4pfRMRA.mp4 4.3 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. DataVis L5C06 V2-BzzTlWHMyV0.mp4 4.3 MB
  • Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.mp4 4.3 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/08. Know Your Audience-OjmrU5HlFD8.mp4 4.3 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/43. Outro V1 V4-XE3aoYOXeBw.mp4 4.3 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/26. Why Are Sampling Distributions Important-aDFDOCJKoH0.mp4 4.3 MB
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  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.mp4 4.3 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/02. L2 2 02 Testing V1 V1-IkLUUHt_jis.mp4 4.3 MB
  • Part 06-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.mp4 4.2 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. L4 071 Box Plots V4-3gxJag12T0g.mp4 4.2 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/12. SVD Practice Takeaways-2er0HUDum7k.mp4 4.2 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/24. Working With Missing Values-mbAgYicmzqE.mp4 4.2 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.2 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.2 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/19. Bag Of Words-A7M1z8yLl0w.mp4 4.2 MB
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.mp4 4.2 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/02. CRISP-DM-PaVwnGcqlSE.mp4 4.2 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.mp4 4.2 MB
  • Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.mp4 4.2 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.mp4 4.2 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/15. Conclusions-3IFF1GzUq0Y.mp4 4.2 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.mp4 4.2 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/02. First Things First-ehjC7JK-zMI.mp4 4.2 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/02. Lesson Overview -q1beUVlLoIQ.mp4 4.2 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/27. L2 04 Tuples V3-33xN-AbTMoc.mp4 4.2 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/02. Why Use Separate Tables-UIQBtpmqYOs.mp4 4.1 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.1 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.1 MB
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.mp4 4.1 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/06. Why Do Analysts Like SQL-uCNOtUht2Xc.mp4 4.1 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.mp4 4.1 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/21. L2 18 Version Control Git Branches V1 V2-C92YcuwjZOs.mp4 4.1 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.mp4 4.1 MB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/01. L6 011 Intro V1-gLy8qpursJI.mp4 4.1 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/12. Types Of Collaborative Filtering-fZhkWHHP6SM.mp4 4.1 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.mp4 4.1 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 4.1 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.mp4 4.1 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.mp4 4.1 MB
  • Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.mp4 4.1 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/25. DATE Functions I-E7Z6GMFVmIY.mp4 4.0 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. L3 011 Intro V3-4BpAF4MYKm8.mp4 4.0 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/13. Minimizing Error Functions-RbT2TXN_6tY.mp4 4.0 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.mp4 4.0 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 4.0 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 4.0 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. A Little More History - A Computer Scientist's Perspective-sVT9nX6HTyU.mp4 4.0 MB
  • Part 12-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.mp4 4.0 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/34. Scaling Data-OgjTk3XCUUE.mp4 4.0 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/02. NULLs-WYUkLKn6XCw.mp4 4.0 MB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.mp4 4.0 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.mp4 4.0 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.mp4 4.0 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/26. GloVe-KK3PMIiIn8o.mp4 4.0 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.mp4 4.0 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/04. L3 Git And Github WalkThrough V1-buMNfXkj9fg.mp4 4.0 MB
  • Part 04-Module 01-Lesson 04_PCA/02. Lesson Topics-LBzA08F_r4w.mp4 4.0 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.mp4 4.0 MB
  • Part 06-Module 01-Lesson 07_Pandas/04. Pandas 1 V1-iXnYN8cnhzs.mp4 4.0 MB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/07. Outro-ot4fPX1jzOI.mp4 4.0 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.mp4 4.0 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.mp4 4.0 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.mp4 4.0 MB
  • Part 06-Module 01-Lesson 07_Pandas/05. Pandas 2 V1-B7MuFIwboKU.mp4 4.0 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/19. The Data Science Process Modeling-bzR6HQBn5CA.mp4 3.9 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.9 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.9 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.mp4 3.9 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Data Vis L4 C03 V1-0F6ldBC6Nbs.mp4 3.9 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.mp4 3.9 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. Data Vis L4 C13 V1-Z7NjwA6jbjU.mp4 3.9 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. Data Vis L4 C09 V1-OnzWhpgM9Vs.mp4 3.9 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.mp4 3.9 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.mp4 3.9 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.mp4 3.9 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.mp4 3.9 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.mp4 3.9 MB
  • Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.mp4 3.9 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.mp4 3.9 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.mp4 3.8 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.mp4 3.8 MB
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.mp4 3.8 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/20. Calculating the p-value-_W3Jg7jQ8jI.mp4 3.8 MB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/06. L6 061 Polishing Plots V3-4TixzVx79uk.mp4 3.8 MB
  • Part 15-Module 01-Lesson 06_Web Development/02. L4 Lesson Overview V2-9WQF-CCNdJ8.mp4 3.8 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.mp4 3.8 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.mp4 3.8 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.mp4 3.8 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4 3.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.mp4 3.8 MB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.mp4 3.8 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/14. Performance Tuning Motivation-aY4_uYWEuoE.mp4 3.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.mp4 3.8 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Data Vis L4 C02 V1-wBDC5AmYgyg.mp4 3.8 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers-TBxUCQdXRjY.mp4 3.7 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.mp4 3.7 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/08. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4 3.7 MB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 26 Branching Overview-ywcOC6CLG4s.mp4 3.7 MB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.mp4 3.7 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.7 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.7 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/16. 04 Inline Comments V1--G6yg3Xhl8I.mp4 3.7 MB
  • Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.mp4 3.7 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.mp4 3.7 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. Inheritance-1gsrxUwPI40.mp4 3.7 MB
  • Part 06-Module 01-Lesson 07_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.mp4 3.7 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/06. Onward-iXbMaTwfIJI.mp4 3.7 MB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.mp4 3.7 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/27. Dummy Variables-bgxBUvPpKQQ.mp4 3.7 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.mp4 3.7 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 0424 V1-x-End5px36M.mp4 3.7 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.mp4 3.7 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.7 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/29. Conclusion-zX5jZH2y8d8.mp4 3.7 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/02. Identifying Recommendation Engines-KwegrgvV-V4.mp4 3.6 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.mp4 3.6 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/16. Inheritance Gaussian Class-XS4LQn1VA3U.mp4 3.6 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/18. Feature Extraction-UgENzCmfFWE.mp4 3.6 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.6 MB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/10. L6 131 Lesson Summary V1-t6ss31RZF34.mp4 3.6 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4 3.6 MB
  • Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.mp4 3.6 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/08. Grid Search SC V1-zDw-ZGiHW5I.mp4 3.6 MB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.mp4 3.6 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-O-4qRh74rkI.mp4 3.6 MB
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.mp4 3.6 MB
  • Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.mp4 3.6 MB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.mp4 3.6 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/17. Ud206 022 Shell Workshop Outro-68twTPXPrx0.mp4 3.6 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/01. Natural Language Processing-UQBxJzoCp-I.mp4 3.6 MB
  • Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.mp4 3.5 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4 3.5 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.mp4 3.5 MB
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.mp4 3.5 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.mp4 3.5 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.mp4 3.5 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. L4 011 Intro V2-JzvJIWG8Rk4.mp4 3.5 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression-Metrics-906P4BPnl9A.mp4 3.5 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.mp4 3.5 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/04. Types Of Sampling-GF_eQqNoarI.mp4 3.5 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. L4 061 Violin Plots 2 V3-0hr61L-LZyM.mp4 3.5 MB
  • Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.mp4 3.5 MB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.mp4 3.5 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.mp4 3.5 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.5 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.5 MB
  • Part 15-Module 01-Lesson 06_Web Development/03. L4 Components Of A Web App V4-2aJf5sO2ox4.mp4 3.5 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.mp4 3.5 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.mp4 3.5 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.mp4 3.5 MB
  • Part 04-Module 01-Lesson 01_Clustering/04. 04 KMeans Use Cases 1 1 V2-25paySwVdAA.mp4 3.4 MB
  • Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Adding A Commit On GitHub-UBYxcTg6VLU.mp4 3.4 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.4 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.mp4 3.4 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Data Vis L4 C04 V1-O6ElT4IFXc0.mp4 3.4 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.mp4 3.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/20. 28 Missing Data Causes V1 V2-zlw8ESS6Q88.mp4 3.4 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/01. Intro-svCesgAQ46Q.mp4 3.4 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.mp4 3.4 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.mp4 3.4 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/01. Intro-j5RmK0UHOTY.mp4 3.4 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.4 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization-4Ieotbeh4u8.mp4 3.4 MB
  • Part 04-Module 01-Lesson 04_PCA/17. When to Use PCA-arSP83-CM6w.mp4 3.4 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/04. Practical Significance-eJ3idt3AJ7E.mp4 3.4 MB
  • Part 12-Module 01-Lesson 14_Regression/21. Recap-DzMi27LI5l4.mp4 3.4 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/23. 24 Conclusion V1 V2-Jq6pj_uKDmY.mp4 3.4 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. L5 081 Plot Matrices V3-2wY-euTIE5g.mp4 3.3 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.mp4 3.3 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.mp4 3.3 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.mp4 3.3 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.mp4 3.3 MB
  • Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.mp4 3.3 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Data Vis L4 C11 V1-3Ls6w8Cd8n4.mp4 3.3 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.mp4 3.3 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/06. Normalization-eOV2UUY8vtM.mp4 3.3 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/14. Ud206 018 P12 Startup Files (.bash_profile)--zF-XebfzBE.mp4 3.3 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.mp4 3.3 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4 3.3 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Layers-pg99FkXYK0M.mp4 3.3 MB
  • Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.mp4 3.3 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/12. Ud206 016 Shell P10 - Searching And Pipes-AWpVScp9z4s.mp4 3.2 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/12. 12 Pipelines And Feature Unions V1 V3-zduxy0g23L0.mp4 3.2 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.mp4 3.2 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. DataVis L5C09 V1-xlZ9AMV6VUE.mp4 3.2 MB
  • Part 15-Module 01-Lesson 06_Web Development/32. L4 Outro V2-8MyuJx5yu38.mp4 3.2 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.2 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/15. Captivate Your Audience - End With A Call To Action-EajX2NbHJ6w.mp4 3.2 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.mp4 3.2 MB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4 3.2 MB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/09. L6 10 V1 V6-LoYT4NMSPGk.mp4 3.2 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.mp4 3.2 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/39. 47 Load V1 V1-Us1hWDaabxo.mp4 3.2 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.mp4 3.2 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/20. Using Grid Search-iTL43Jk9_bQ.mp4 3.2 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.mp4 3.2 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/23. L2 18 Version Control Merging Branches On A Team V1 V2-36DOnNzvT4A.mp4 3.2 MB
  • Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.mp4 3.2 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.mp4 3.2 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/01. Lesson Introduction-rw3YaQ2CTNQ.mp4 3.2 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.2 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/08. L2 2 11 Logging V2-9qKQdRoIMbU.mp4 3.2 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.mp4 3.2 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Data Vis L4 C06 V2-f8Kh4PByiEA.mp4 3.2 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.2 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.2 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.mp4 3.2 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. L3 - Include Upstream Changes-VvoC6hN6FjU.mp4 3.2 MB
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.mp4 3.1 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/18. L2 181 Lesson Summary HDmp4 V3-kKEeBDs4HuM.mp4 3.1 MB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/22. Virtual Environments-f7rzxUiHOJ0.mp4 3.1 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/27. Goals Of Recommendation Systems-WzelOlFeDmU.mp4 3.1 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/25. Word2Vec-7jjappzGRe0.mp4 3.1 MB
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.mp4 3.1 MB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4 3.1 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/07. Latent Factors-jZz7tFEF2Dc.mp4 3.1 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.mp4 3.1 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.mp4 3.1 MB
  • Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.mp4 3.1 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.mp4 3.1 MB
  • Part 04-Module 01-Lesson 04_PCA/07. Dimensionality Reduction-mANti9veGtc.mp4 3.1 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.mp4 3.1 MB
  • Part 15-Module 01-Lesson 06_Web Development/26. Flask Pandas Plotly Part3-e8owK5zk-g8.mp4 3.1 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.mp4 3.1 MB
  • Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.mp4 3.1 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.mp4 3.1 MB
  • Part 15-Module 01-Lesson 06_Web Development/07. Div and Span-cbKA_dvthcY.mp4 3.1 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.mp4 3.1 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4 3.1 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment-fH_xF5_SDCE.mp4 3.0 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/19. Conclusion-_ATzG6khLdk.mp4 3.0 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 3.0 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.mp4 3.0 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. L3 081 Histograms V2-RLez9L0htGQ.mp4 3.0 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.mp4 3.0 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.mp4 3.0 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/01. 01 Intro V1 V3-Zl_es7xtSqk.mp4 3.0 MB
  • Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.mp4 3.0 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. INTRODUÇÃO AO PROJETO PRINCIPAL V2---9IFCNBM6Y.mp4 3.0 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/09. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4 3.0 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.mp4 3.0 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.mp4 3.0 MB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.mp4 3.0 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/25. L2 21 Conclusion V1 V1-anPnokWZOZQ.mp4 3.0 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/21. Closed Form Solution-G3fRVgLa5gI.mp4 3.0 MB
  • Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.mp4 3.0 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/06. Up and Running On Medium-0QzbxjAcMq0.mp4 3.0 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 3.0 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 3.0 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.mp4 3.0 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.mp4 3.0 MB
  • Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.mp4 3.0 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.mp4 3.0 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.mp4 2.9 MB
  • Part 12-Module 01-Lesson 14_Regression/02. Introduction to Machine Learning-pLcFPPI1L-0.mp4 2.9 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/04. Types Of Machine Learning - Supervised-Jn3xugBvs2U.mp4 2.9 MB
  • Part 08-Module 01-Lesson 07_Visualization Case Study/07. L7 0F1 Congrats V3-LF-obnL7CI0.mp4 2.9 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.mp4 2.9 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.mp4 2.9 MB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.mp4 2.9 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.mp4 2.9 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.mp4 2.9 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.mp4 2.9 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.mp4 2.9 MB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/10. Three Steps To Captivate Your Audience-BWS3oQYS-c4.mp4 2.9 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/01. 01 Intro-4C4PuJANIdE.mp4 2.9 MB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.mp4 2.9 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. L5 051 Faceting In Two Directions V3-lz5dcoTcV2o.mp4 2.8 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.mp4 2.8 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/14. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4 2.8 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.mp4 2.8 MB
  • Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.mp4 2.8 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/10. The Data Science Process Gathering And Wrangling-GvyfIiJUXWg.mp4 2.8 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.mp4 2.8 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions--UvpQV1qmiE.mp4 2.8 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/10. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4 2.8 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/07. Knowledge Based Recommendations-C_vU1tjQHZI.mp4 2.8 MB
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.mp4 2.8 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.7 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.mp4 2.7 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.mp4 2.7 MB
  • Part 17-Module 02-Lesson 03_AB Testing Case Study/01. Intro-28mN6RvGXDM.mp4 2.7 MB
  • Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.mp4 2.7 MB
  • Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.mp4 2.7 MB
  • Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.mp4 2.7 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.mp4 2.7 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.mp4 2.7 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4 2.7 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.mp4 2.7 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.mp4 2.7 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/10. Mean Absolute Error-vLKiY0Ehors.mp4 2.7 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.mp4 2.7 MB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.mp4 2.7 MB
  • Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.mp4 2.7 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.mp4 2.7 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.mp4 2.7 MB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.mp4 2.7 MB
  • Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.mp4 2.7 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/20. 02 TF-IDF-LYYWIrWbBq4.mp4 2.7 MB
  • Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.mp4 2.6 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.mp4 2.6 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.mp4 2.6 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.mp4 2.6 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.mp4 2.6 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.mp4 2.6 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/06. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4 2.6 MB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.mp4 2.6 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.mp4 2.6 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.mp4 2.6 MB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.mp4 2.6 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.mp4 2.6 MB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/08. L1 08.1 Lesson Summary HD (1)--c9IeqHkAZ0.mp4 2.6 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Theory-H5JqcdIB5y0.mp4 2.6 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.mp4 2.5 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.5 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.mp4 2.5 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.mp4 2.5 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.mp4 2.5 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.mp4 2.5 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.mp4 2.5 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.mp4 2.5 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.mp4 2.5 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/07. Scikit Learn-kxvmG8ZsOVg.mp4 2.5 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/04. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.5 MB
  • Part 04-Module 01-Lesson 04_PCA/06. How to Reduce Features-ydhrelgjriI.mp4 2.4 MB
  • Part 17-Module 02-Lesson 03_AB Testing Case Study/14. Conclusion-2G6x3oQnjy4.mp4 2.4 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/09. Experiment II-fq4eO7CybA4.mp4 2.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/05. 05 Extraction Idea 1 V1 V2-4dKG_08zMm4.mp4 2.4 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.mp4 2.4 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.mp4 2.4 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.mp4 2.4 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.mp4 2.4 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.mp4 2.4 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.4 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.4 MB
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.mp4 2.4 MB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.mp4 2.4 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.mp4 2.4 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.mp4 2.4 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/11. 06 Precision SC V1-q2wVorBfefU.mp4 2.4 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.mp4 2.3 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.mp4 2.3 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.mp4 2.3 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/10. Answer False Negatives And Positives-KOytJL1lvgg.mp4 2.3 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4 2.3 MB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.mp4 2.3 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.mp4 2.3 MB
  • Part 15-Module 01-Lesson 01_Introduction to Software Engineering/02. L1 02 Course Overview V1 V4-v-DB0W_I2n8.mp4 2.3 MB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.mp4 2.3 MB
  • Part 14-Module 01-Lesson 01_The Data Science Process/20. Predicting Salary-g1ZAn02ETK4.mp4 2.3 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4 2.3 MB
  • Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.mp4 2.3 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.mp4 2.3 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/06. Ud206 007 Shell P4 - Current Working Directory-X7dsy3oMHp0.mp4 2.3 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/04. Intro To MovieTweetings-cuXvLIkq_W8.mp4 2.3 MB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/05. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.3 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.mp4 2.3 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.mp4 2.3 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.mp4 2.3 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. L5 091 Feature Engineering V2-jpMOSFMMga4.mp4 2.3 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.mp4 2.3 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/22. Having-D4gmN0vnk58.mp4 2.3 MB
  • Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.3 MB
  • Part 04-Module 01-Lesson 01_Clustering/03. Two Types of Unsupervised Learning-aHK_rpaS_ts.mp4 2.3 MB
  • Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.mp4 2.3 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/12. Part-of-Speech Tagging-WFEu8bXI5OA.mp4 2.3 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/08. When Accuracy Wont Work-r0-O-gIDXZ0.mp4 2.3 MB
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.mp4 2.3 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/12. 07 Recall SC V1-0n5wUZiefkQ.mp4 2.3 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.mp4 2.2 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/15. Momentum-r-rYz_PEWC8.mp4 2.2 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.mp4 2.2 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.mp4 2.2 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.mp4 2.2 MB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/01. L2 011 Intro HD V2-TlpGWQBLG6E.mp4 2.2 MB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.mp4 2.2 MB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.mp4 2.2 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. L5 061 Other Adaptations Of Bivariate Plots V3-qanSZttNzFM.mp4 2.2 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.mp4 2.2 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/03. Your First JOIN-HkX9fkNRbU8.mp4 2.2 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/27. WHERE with Non-Numeric Data-_pLx7MHOyjo.mp4 2.2 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 2-x7foG7murvU.mp4 2.2 MB
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  • Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.mp4 2.2 MB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.mp4 2.2 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.mp4 2.2 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/04. Ud206 004 Shell P2 - Your First Command-ggf5WhOYy1U.mp4 2.2 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.mp4 2.2 MB
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  • Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.2 MB
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  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.mp4 2.2 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4 2.2 MB
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.mp4 2.2 MB
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  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.mp4 2.2 MB
  • Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.mp4 2.2 MB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.mp4 2.2 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.mp4 2.1 MB
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  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.mp4 2.1 MB
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  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. 13 Inheritance Example V1-uWT-HIHBjv0.mp4 2.1 MB
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.mp4 2.1 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/10. Stop Word Removal-WAU_Ij0GJbw.mp4 2.1 MB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 1-SNFHYbJvlZU.mp4 2.0 MB
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  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.mp4 2.0 MB
  • Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.mp4 2.0 MB
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.mp4 2.0 MB
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  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.mp4 2.0 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.mp4 2.0 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 2.0 MB
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  • Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.mp4 1.9 MB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.mp4 1.9 MB
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  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.mp4 1.9 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/05. Ud206 006 Shell P3 - Navigating Directories-i9Xp94DmdB8.mp4 1.9 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.mp4 1.9 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4 1.9 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.mp4 1.9 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.mp4 1.9 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.mp4 1.9 MB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/20. L2 02 Outro REPLACEMENT-W-6Se0G_FVE.mp4 1.9 MB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/17. Funk SVD Review-nc3GMIrISHE.mp4 1.9 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-6LO6I5M18PQ.mp4 1.9 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.mp4 1.8 MB
  • Part 02-Module 01-Lesson 09_Training and Tuning/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4 1.8 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.mp4 1.8 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.mp4 1.8 MB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/07. Categorical Ordinal Nominal Data-k5bLaPGY2Vw.mp4 1.8 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/img/screen-shot-2018-05-29-at-4.06.53-pm.png 1.8 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.mp4 1.8 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.mp4 1.8 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/16. Error Functions Around the World-34AAcTECu2A.mp4 1.8 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4 1.8 MB
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  • Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.8 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.8 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4 1.8 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.mp4 1.8 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.mp4 1.8 MB
  • Part 04-Module 01-Lesson 01_Clustering/05. KMeans-B9jdQFpPEk0.mp4 1.8 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.mp4 1.8 MB
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.mp4 1.8 MB
  • Part 04-Module 01-Lesson 04_PCA/04. Latent Features-kYLcVgpEwGs.mp4 1.8 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.mp4 1.8 MB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-09-21-at-11.36.43-am.png 1.8 MB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/17. 05 Docstrings V1-_gapemxsRJY.mp4 1.7 MB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Theory-twLr9ndsf90.mp4 1.7 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.7 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.7 MB
  • Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.mp4 1.7 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.mp4 1.7 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.mp4 1.7 MB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4 1.7 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.7 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.7 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.mp4 1.7 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.mp4 1.7 MB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. DataVis L5C05 V1-v19gCP4TvwE.mp4 1.7 MB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.mp4 1.7 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.mp4 1.7 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.mp4 1.7 MB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes Pt 1-HPmMEkbT2uE.mp4 1.7 MB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4 1.7 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.mp4 1.7 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.mp4 1.7 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.mp4 1.6 MB
  • Part 01-Module 02-Lesson 02_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.48.22-pm.png 1.6 MB
  • Part 13-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.48.22-pm.png 1.6 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.mp4 1.6 MB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4 1.6 MB
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.mp4 1.6 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.mp4 1.6 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.6 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.6 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.mp4 1.6 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.mp4 1.6 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.mp4 1.6 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.mp4 1.6 MB
  • Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.6 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.mp4 1.5 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.mp4 1.5 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.mp4 1.5 MB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 71 Merging-gQiWicrreJg.mp4 1.5 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/11. Transform Walk Through-i9_0kHCCCCE.mp4 1.5 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.mp4 1.5 MB
  • Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.mp4 1.5 MB
  • Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4 1.5 MB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.mp4 1.5 MB
  • Part 08-Module 01-Lesson 07_Visualization Case Study/01. L7 011 Intro V1-Virihwp36do.mp4 1.5 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.mp4 1.5 MB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/20. Outro SC V1-YD1grQje9fw.mp4 1.5 MB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.mp4 1.4 MB
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.mp4 1.4 MB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/11. Intro To Collab Filtering-wGq7dUgJpsc.mp4 1.4 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.4 MB
  • Part 20-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.4 MB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/41. 52 Putting It All Together V1 1 V1-D2Th0KdPI-Y.mp4 1.4 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.mp4 1.4 MB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. DataVis L3 11 V1-C8DGwJa_adA.mp4 1.4 MB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/11. Vanishing Gradient-W_JJm_5syFw.mp4 1.4 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.mp4 1.4 MB
  • Part 09-Module 01-Lesson 01_Shell Workshop/10. Ud206 013 Shell P8 - Viewing Files-hPPVMKqbQV0.mp4 1.4 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/03. Entity Relationship Diagrams-YY2TAJLEINA.mp4 1.4 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/16. GROUP BY Part II-0HQ-TshNNQA.mp4 1.4 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.mp4 1.4 MB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/img/screen-shot-2018-05-29-at-4.19.03-pm.png 1.4 MB
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.mp4 1.4 MB
  • Part 10-Module 01-Lesson 07_Working With Remotes/06. L1 - Fetch Merge And Push-jwyQUfE1Eqw.mp4 1.4 MB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/04. COUNT-b4FCWAEGmLg.mp4 1.4 MB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.mp4 1.3 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.mp4 1.3 MB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/03. What is the Difference-I3tQvrCgNrQ.mp4 1.3 MB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.mp4 1.3 MB
  • Part 07-Module 01-Lesson 02_SQL Joins/10. ALIAS-viWHJaxWTvw.mp4 1.3 MB
  • Part 07-Module 01-Lesson 01_Basic SQL/07. How Databases Store Data-H0C9z_sRvLE.mp4 1.3 MB
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.mp4 1.3 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.mp4 1.3 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/23. Word Embeddings-4mM_S9L2_JQ.mp4 1.3 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.mp4 1.3 MB
  • Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/05. Outro-dVrYQ7o8a-k.mp4 1.3 MB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.mp4 1.3 MB
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.mp4 1.3 MB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/05. Machine Learning Workflow-0nA6oTIlwaA.mp4 1.3 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/24. Modeling-P4w_2rkxBvE.mp4 1.2 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.mp4 1.2 MB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/13. Named Entity Recognition-QUQu2nsE7vE.mp4 1.2 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.mp4 1.2 MB
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.mp4 1.2 MB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.mp4 1.2 MB
  • Part 04-Module 01-Lesson 05_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.mp4 1.2 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.2 MB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.2 MB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.mp4 1.2 MB
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  • Part 04-Module 01-Lesson 05_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.mp4 1.1 MB
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  • Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.mp4 825.8 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/conda_default_install.mp4 609.6 kB
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  • Part 12-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2018-11-19-at-11.32.05-am.png 533.6 kB
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  • Part 02-Module 01-Lesson 02_Linear Regression/img/house.png 503.3 kB
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  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/img/screen-shot-2018-04-29-at-10.10.52-am.png 498.7 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/img/screen-shot-2018-06-02-at-6.07.54-pm.png 476.9 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/img/3030118734.gif 471.1 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/img/6485174133.gif 469.1 kB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/img/ud456-l2-04-new-issue-button.png 467.2 kB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-watched-repos.png 461.7 kB
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  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/img/screen-shot-2018-03-19-at-2.49.57-pm.png 453.1 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/tidy-data-three.png 448.1 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-search.png 441.2 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-dyShKWpTo-c.mp4 429.0 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/img/3043028606.gif 418.0 kB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/img/ud123-l3-git-log-vs-git-log-stat.png 414.0 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/tidy-data-four.png 407.4 kB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.mp4 404.5 kB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.mp4 404.5 kB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/img/screen-shot-2018-06-02-at-5.34.36-pm.png 404.1 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/or-quiz.png 403.1 kB
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  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/img/mat-leonard-circle.png 394.1 kB
  • Part 10-Module 01-Lesson 01_What is Version Control/img/ud123-l1-git-course-outline.png 387.5 kB
  • Part 13-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.49.50-pm.png 384.6 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/img/3021738574.gif 384.0 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/tidy-data-two.png 380.7 kB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/img/margin-geometry-images.008.jpeg 378.3 kB
  • Part 13-Module 01-Lesson 02_Get Help from Peers and Mentors/img/screen-shot-2018-11-07-at-10.23.07-pm.png 374.8 kB
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  • Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.14.23-pm.png 367.2 kB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/img/bad-viz-2.png 365.0 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/Markdown+cells.mp4 338.3 kB
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  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/img/screen-shot-2018-09-14-at-10.16.10-am.png 253.6 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/img/3050008540.gif 245.8 kB
  • Part 01-Module 02-Lesson 02_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.49.34-pm.png 244.7 kB
  • Part 13-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.49.34-pm.png 244.7 kB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/screen-shot-2018-05-26-at-7.24.13-pm.png 244.0 kB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/img/screen-shot-2018-09-14-at-10.11.13-am.png 242.6 kB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-07.png 238.9 kB
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  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/redacted-linkedinresults.png 236.3 kB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/img/screen-shot-2017-11-16-at-3.54.06-pm.png 235.3 kB
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  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-03.png 234.4 kB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/recall-quiz.png 233.7 kB
  • Part 13-Module 01-Lesson 02_Get Help from Peers and Mentors/img/image8.png 233.5 kB
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  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/img/screen-shot-2017-12-07-at-3.45.19-pm.png 232.6 kB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/img/margin-geometry-images.001.jpeg 231.0 kB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-status-with-untracked.png 228.3 kB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-08-13-at-6.39.12-pm.png 228.2 kB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-status-after-git-add.png 227.6 kB
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  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/full-padding-no-strides-transposed.gif 227.1 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-07-at-4.35.30-pm.png 225.6 kB
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.mp4 225.6 kB
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-02.png 224.5 kB
  • Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-2.24.21-pm.png 224.0 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.mp4 223.5 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-11-at-12.52.21-pm.png 222.5 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/notebook+interface.mp4 220.6 kB
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  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/img/xor.png 220.1 kB
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  • Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-24-at-2.18.30-pm.png 216.4 kB
  • Part 03-Module 01-Lesson 04_Keras/img/meme.png 214.1 kB
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  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-status-modified-files.png 213.5 kB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/img/mike-josh-bios-portraits.png 213.3 kB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/img/ud123-l3-git-log-stat.gif 211.7 kB
  • Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-.git-directory.png 210.7 kB
  • Part 11-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-24-at-3.13.49-pm.png 209.5 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/conda_install.mp4 206.6 kB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/img/screen-shot-2018-07-19-at-4.05.25-pm.png 206.1 kB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-02-23-at-5.11.40-pm.png 205.5 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/img/batch-stochastic.png 201.6 kB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-merge-conflict.png 198.4 kB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-ignore-word-doc.png 197.4 kB
  • Part 02-Module 01-Lesson 04_Decision Trees/img/table.png 196.7 kB
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  • Part 08-Module 01-Lesson 02_Design of Visualizations/img/pasted-image-0.png 196.4 kB
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/confusion.png 193.4 kB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/img/screen-shot-2018-01-03-at-2.23.38-pm.png 192.4 kB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/medical.png 191.0 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-git-remote-from-clone.png 190.7 kB
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  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-tag-delete.png 184.7 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/img/mat-headshot.png 184.3 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/mat-headshot.png 184.3 kB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-diff.png 183.8 kB
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  • Part 10-Module 01-Lesson 03_Review a Repo's History/img/ud123-l3-git-status-output.png 178.4 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/img/quiz.jpg 178.4 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-07-at-6.02.41-pm.png 177.3 kB
  • Part 04-Module 01-Lesson 05_Random Projection and ICA/img/eeg-ica.png 175.0 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2018-01-24-at-12.03.45-am.png 174.9 kB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-08-13-at-6.26.18-pm.png 173.3 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/command+palette.mp4 173.2 kB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-status.png 171.6 kB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-changes-add-color.png 168.1 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-timeit.png 161.1 kB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-05-25-at-11.27.36-am.png 160.4 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/server-shutdown.png 159.2 kB
  • Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-prep.png 158.8 kB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/img/ud456-l2-02-git-fork-error.png 158.7 kB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/img/challenger2.gif 158.3 kB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-checkout-sidebar.png 157.9 kB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-rename-repos.png 156.9 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-11-at-12.52.03-pm.png 156.3 kB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-05-11-at-11.03.34-am.png 154.6 kB
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/email.png 152.1 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-04-commit-count-local.png 151.1 kB
  • Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-clone.gif 150.9 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-git-remote-shortname.png 150.6 kB
  • Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-submit.png 149.7 kB
  • Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-gpu.png 149.0 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-my-travel-plans-project.png 148.8 kB
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  • Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-notebook.png 146.3 kB
  • Part 17-Module 04-Lesson 01_Recommendation Engines/img/screen-shot-2018-09-17-at-3.40.30-pm.png 145.0 kB
  • Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/img/screen-shot-2018-02-21-at-8.05.18-pm.png 144.8 kB
  • Part 02-Module 01-Lesson 04_Decision Trees/img/recommending-apps.png 143.9 kB
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  • assets/css/bootstrap.min.css 140.9 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/img/minibatch.png 140.0 kB
  • Part 02-Module 01-Lesson 05_Naive Bayes/img/spamham.png 138.3 kB
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  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/img/screen-shot-2018-07-19-at-4.06.55-pm.png 133.1 kB
  • assets/js/plyr.polyfilled.min.js 129.2 kB
  • Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.58.00-pm.png 129.1 kB
  • Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-mixed.png 128.9 kB
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  • Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.57.42-pm.png 117.0 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-tab.png 112.6 kB
  • Part 02-Module 01-Lesson 09_Training and Tuning/img/learning-curves.png 111.6 kB
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  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/media/unnamed-project-desc-0.gif 96.8 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/img/xor-quiz.png 96.4 kB
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  • Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-menu.png 96.2 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/img/polynomial-kernel-2-quiz.png 81.5 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-download.png 81.5 kB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/img/likertscale.png 81.4 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/img/screen-shot-2017-08-02-at-11.14.25-am.png 81.3 kB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/img/screen-shot-2018-09-14-at-2.25.01-pm.png 81.2 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/img/48230510.gif 81.1 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/matrix-mult-3.png 80.9 kB
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/img/48678737.gif 79.6 kB
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  • Part 12-Module 01-Lesson 07_Bayes Rule/img/48737119.gif 79.0 kB
  • Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-init.gif 77.7 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/img/48686674.gif 77.6 kB
  • Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/img/disaster-response-project1.png 76.5 kB
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  • Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-post.png 75.9 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/nbconvert-example.png 75.1 kB
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/img/48746014.gif 74.4 kB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/img/screen-shot-2018-03-09-at-4.07.07-pm.png 73.8 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/gradient-descent.png 73.7 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-create-env.png 72.5 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-pdb.png 70.3 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/img/just-a-2d-reg.png 70.1 kB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/img/48734186.gif 70.0 kB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/img/48680638.gif 70.0 kB
  • Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-10-at-8.10.13-pm.png 69.8 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-nav-bar-new-repo-link.png 69.8 kB
  • Part 02-Module 01-Lesson 05_Naive Bayes/img/spam.png 69.4 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/right-join.png 68.0 kB
  • Part 02-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-01-06-at-9.30.27-pm.png 68.0 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/left-join.png 67.9 kB
  • Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-29-at-11.49.47-am.png 67.0 kB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/img/ud456-l2-02-clone-linked-to-fork.png 66.9 kB
  • Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-status-new-project.gif 66.9 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48739104.gif 65.4 kB
  • Part 11-Module 01-Lesson 01_Introduction/img/cp1a9390.jpg 65.2 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-shutdown.png 63.8 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/full-outer-join-if-null.png 63.5 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/slides-cell-toolbar-menu.png 62.8 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/full-outer-join.png 62.6 kB
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48692666.gif 61.0 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/network-with-labeled-weights.png 60.9 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-timeit2.png 57.5 kB
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  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/derivative-example.png 56.4 kB
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/img/48729170.gif 55.7 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/slides-choose-slide-type.png 54.6 kB
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  • Part 12-Module 01-Lesson 04_Probability/img/48698595.gif 54.3 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/img/screen-shot-2018-01-06-at-8.13.20-pm.png 52.0 kB
  • Part 02-Module 01-Lesson 09_Training and Tuning/img/circle-data.png 51.1 kB
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  • Part 03-Module 01-Lesson 04_Keras/img/data.png 50.7 kB
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  • Part 14-Module 01-Lesson 01_The Data Science Process/33. Imputing Missing Values-CEWIPjz_gCE.en.vtt 3.2 kB
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  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt 3.2 kB
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  • Part 15-Module 01-Lesson 01_Introduction to Software Engineering/01. Introduction To Software Engineering-7kphieW4yl4.en.vtt 3.2 kB
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  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.en.vtt 3.2 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt 3.2 kB
  • Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.pt-BR.vtt 3.2 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.en.vtt 3.2 kB
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  • Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.ar.vtt 3.1 kB
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  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt 3.1 kB
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  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.en.vtt 3.1 kB
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.ar.vtt 3.1 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/04. Fitting Logistic Regression In Python-baQf-XiZQQ4.pt-BR.vtt 3.1 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.ar.vtt 3.1 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.pt-BR.vtt 3.1 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.pt-BR.vtt 3.1 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.es-ES.vtt 3.1 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.zh-CN.vtt 3.1 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.zh-CN.vtt 3.1 kB
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  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. L5 021 Non Positional Encodings For Third Variables V1-D91mm-qaDkk.pt-BR.vtt 3.1 kB
  • Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.ar.vtt 3.1 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.ar.vtt 3.1 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.th.vtt 3.0 kB
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  • Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.pt-BR.vtt 3.0 kB
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.zh-CN.vtt 3.0 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.zh-CN.vtt 3.0 kB
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  • Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.pt-BR.vtt 3.0 kB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.pt-BR.vtt 3.0 kB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.ar.vtt 3.0 kB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/05. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.pt-BR.vtt 3.0 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/41. How To Fix This-IPQZ4pfRMRA.pt-BR.vtt 3.0 kB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.pt-BR.vtt 3.0 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.en.vtt 3.0 kB
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  • Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.pt-BR.vtt 3.0 kB
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  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.pt-BR.vtt 3.0 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.en.vtt 3.0 kB
  • Part 09-Module 01-Lesson 01_Shell Workshop/13. Ud206 017 Shell P11 - Variables-Dx3WlMZk8iA.en.vtt 3.0 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.pt-BR.vtt 3.0 kB
  • Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.pt-BR.vtt 3.0 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.pt-BR.vtt 3.0 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions--UvpQV1qmiE.en.vtt 3.0 kB
  • Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.en.vtt 3.0 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.pt-BR.vtt 3.0 kB
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  • Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.zh-CN.vtt 3.0 kB
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  • Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.en.vtt 3.0 kB
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  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/16. Inheritance Gaussian Class-XS4LQn1VA3U.en.vtt 3.0 kB
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  • Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.en.vtt 3.0 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/41. How To Fix This-IPQZ4pfRMRA.en.vtt 3.0 kB
  • Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.pt-BR.vtt 3.0 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.en.vtt 3.0 kB
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  • Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.zh-CN.vtt 3.0 kB
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  • Part 02-Module 01-Lesson 10_Finding Donors Project/06. Kaggle Project Final For Classroom-Ssttix340C8.pt-BR.vtt 3.0 kB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Kaggle Project Final For Classroom-Ssttix340C8.pt-BR.vtt 3.0 kB
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt 3.0 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.zh-CN.vtt 2.7 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.pt-BR.vtt 2.6 kB
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  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.zh-CN.vtt 2.6 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/11. Mean Squared Error-MRyxmZDngI4.en.vtt 2.5 kB
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  • Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.pt-BR.vtt 2.5 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.ar.vtt 2.5 kB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. L3 02 Proced Vs OOP V1 V3-psXD_J8FnCQ.pt-BR.vtt 2.5 kB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. L4 121 Adaptations Of Univariate Plots V3-MXcqplnUB0o.pt-BR.vtt 2.5 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.ar.vtt 2.5 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.en.vtt 2.5 kB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.pt-BR.vtt 2.5 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.en.vtt 2.5 kB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.en.vtt 2.5 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.ar.vtt 2.5 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.zh-CN.vtt 2.5 kB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.en.vtt 2.5 kB
  • Part 04-Module 01-Lesson 01_Clustering/15. Is That The Optimal Solution-g5aPtCpBNmw.pt-BR.vtt 2.5 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.pt-BR.vtt 2.5 kB
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.zh-CN.vtt 2.5 kB
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers-TBxUCQdXRjY.en.vtt 2.5 kB
  • Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.en.vtt 2.5 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.pt-BR.vtt 2.5 kB
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  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment-fH_xF5_SDCE.en.vtt 2.5 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. A Little More History - A Computer Scientist's Perspective-sVT9nX6HTyU.en.vtt 2.5 kB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.pt-BR.vtt 2.5 kB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.en.vtt 2.5 kB
  • Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt 2.5 kB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.en.vtt 2.5 kB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.pt-BR.vtt 2.5 kB
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  • Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.pt-BR.vtt 2.5 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/27. Goals Of Recommendation Systems-WzelOlFeDmU.en.vtt 2.5 kB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. Inheritance-1gsrxUwPI40.pt-BR.vtt 2.5 kB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.zh-CN.vtt 2.5 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/09. Business And Data Understanding - Part 2-iInjuIgBWIo.en.vtt 2.5 kB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.pt-BR.vtt 2.5 kB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.zh-CN.vtt 2.5 kB
  • Part 02-Module 01-Lesson 07_Ensemble Methods/10. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt 2.5 kB
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  • Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.zh-CN.vtt 2.5 kB
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  • Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.en.vtt 2.5 kB
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  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.pt-BR.vtt 2.5 kB
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  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.pt-BR.vtt 2.5 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt 2.5 kB
  • Part 16-Module 01-Lesson 01_Introduction to Data Engineering/01. 01 Welcome V1 V2-Ykd7CN5dDx0.en.vtt 2.5 kB
  • Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.en.vtt 2.5 kB
  • Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.en.vtt 2.4 kB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/12. SVD Practice Takeaways-2er0HUDum7k.en.vtt 2.4 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/16. Identifying Recommendations-P60qvS_OTMg.en.vtt 2.4 kB
  • Part 02-Module 01-Lesson 05_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt 2.4 kB
  • Part 15-Module 01-Lesson 06_Web Development/18. L4 The Back End V2-Esl0NL63S2c.en.vtt 2.4 kB
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  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.ar.vtt 2.4 kB
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  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt 2.4 kB
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  • Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.zh-CN.vtt 2.4 kB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.pt-BR.vtt 2.4 kB
  • Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/01. Welcome To DSND T2 V1 1 V1-ebJZrc2y85Q.en.vtt 2.4 kB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.zh-CN.vtt 2.4 kB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.zh-CN.vtt 2.4 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.pt-BR.vtt 2.4 kB
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  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.en.vtt 2.4 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.zh-CN.vtt 2.4 kB
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  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt 2.4 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt 2.4 kB
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  • Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.zh-CN.vtt 2.4 kB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.en.vtt 2.4 kB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.ar.vtt 2.4 kB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. L4 111 Faceting V2-oUYRqI6wFGw.en.vtt 2.4 kB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/07. Controlling Variables-pLTneSg2MRY.en.vtt 2.4 kB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.en.vtt 2.4 kB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.pt-BR.vtt 2.4 kB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.en.vtt 2.4 kB
  • Part 15-Module 01-Lesson 06_Web Development/07. Div and Span-cbKA_dvthcY.en.vtt 2.4 kB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. L3 02 Proced Vs OOP V1 V3-psXD_J8FnCQ.en.vtt 2.4 kB
  • Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt 2.4 kB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt 2.4 kB
  • Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.pt-BR.vtt 2.4 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.en.vtt 2.4 kB
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  • Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.zh-CN.vtt 2.4 kB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 07 Lists And Membership Operators II V3-3Nj-b-ZzqH8.pt-BR.vtt 2.4 kB
  • Part 04-Module 01-Lesson 04_PCA/07. Dimensionality Reduction-mANti9veGtc.en.vtt 2.4 kB
  • Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt 2.4 kB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/29. L3 21 Outro v1 V2-DStO1hBKtHQ.pt-BR.vtt 2.4 kB
  • Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.zh-CN.vtt 2.4 kB
  • Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.en.vtt 2.4 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt 2.4 kB
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 0424 V1-x-End5px36M.en.vtt 2.4 kB
  • Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt 2.4 kB
  • Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.en.vtt 2.4 kB
  • Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.pt-BR.vtt 2.4 kB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.pt-BR.vtt 2.4 kB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/22. 30 Imputing Missing Data V1 V3-A5sOJDj3AKg.en.vtt 2.4 kB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.en.vtt 2.4 kB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.ar.vtt 2.4 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt 2.4 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.ar.vtt 2.4 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt 2.4 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.zh-CN.vtt 2.4 kB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.zh-CN.vtt 2.4 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.zh-CN.vtt 2.4 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt 2.4 kB
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  • Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.zh-CN.vtt 2.3 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/02. History - Statisticians Perspective-zNNouqLGF9E.pt-BR.vtt 2.3 kB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c03-practicalsignificance-03.png 2.3 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.zh-CN.vtt 2.3 kB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.pt-BR.vtt 2.3 kB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.en.vtt 2.3 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt 2.3 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.en.vtt 2.3 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt 2.3 kB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.en.vtt 2.3 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt 2.3 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.en.vtt 2.3 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.zh-CN.vtt 2.3 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt 2.3 kB
  • Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.ar.vtt 2.3 kB
  • Part 16-Module 01-Lesson 02_ETL Pipelines/11. 19 Transform Intro V2 V3-SXp4Qa-rQJg.pt-BR.vtt 2.3 kB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.pt-BR.vtt 2.3 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.ar.vtt 2.3 kB
  • Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.pt-BR.vtt 2.3 kB
  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.pt-BR.vtt 2.3 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.en.vtt 2.3 kB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.ar.vtt 2.3 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.pt-BR.vtt 2.3 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.ar.vtt 2.3 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.en.vtt 2.3 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/codecogseqn-2.png 2.3 kB
  • Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.en.vtt 2.3 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.pt-BR.vtt 2.3 kB
  • Part 13-Module 01-Lesson 04_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.en.vtt 2.3 kB
  • Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.zh-CN.vtt 2.3 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.en.vtt 2.3 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/11. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt 2.3 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.zh-CN.vtt 2.3 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.en.vtt 2.3 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.ar.vtt 2.3 kB
  • Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.en.vtt 2.3 kB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.en.vtt 2.3 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.zh-CN.vtt 2.3 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.en.vtt 2.3 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.zh-CN.vtt 2.3 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.pt-BR.vtt 2.3 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.pt-BR.vtt 2.3 kB
  • Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt 2.3 kB
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/26. Types Of Ratings-fMjqe4sxBlQ.en.vtt 2.3 kB
  • Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.ar.vtt 2.3 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.ar.vtt 2.3 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.en.vtt 2.3 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt 2.3 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.en.vtt 2.3 kB
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  • Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.pt-BR.vtt 2.2 kB
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.en-US.vtt 2.1 kB
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  • Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.en.vtt 2.1 kB
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  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.pt-BR.vtt 2.0 kB
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  • Part 12-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.pt-BR.vtt 2.0 kB
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  • Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.en.vtt 2.0 kB
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  • Part 14-Module 01-Lesson 01_The Data Science Process/18. It Is Not Always About ML-ECqflypBU7M.pt-BR.vtt 2.0 kB
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.en.vtt 2.0 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/03. The Data Science Process Business And Data Understanding-eG_jKQezhc4.pt-BR.vtt 2.0 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/03. The Data Science Process Business And Data Understanding-eG_jKQezhc4.en.vtt 2.0 kB
  • Part 15-Module 01-Lesson 01_Introduction to Software Engineering/03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.pt-BR.vtt 2.0 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.pt-BR.vtt 2.0 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.th.vtt 2.0 kB
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  • Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.pt-BR.vtt 2.0 kB
  • Part 04-Module 01-Lesson 01_Clustering/07. 07 Changing K 1 V3-Bd3M-xUlqEI.pt-BR.vtt 2.0 kB
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  • Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.zh-CN.vtt 2.0 kB
  • Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.zh-CN.vtt 2.0 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/19. The Data Science Process Modeling-bzR6HQBn5CA.en.vtt 2.0 kB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/17. Other Important Information-LF-CWF-1mX4.en.vtt 2.0 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.pt-BR.vtt 2.0 kB
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  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.ar.vtt 2.0 kB
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  • Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.en.vtt 1.8 kB
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  • Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.zh-CN.vtt 1.8 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.ar.vtt 1.8 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.en.vtt 1.8 kB
  • Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.zh-CN.vtt 1.8 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.zh-CN.vtt 1.8 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.zh-CN.vtt 1.8 kB
  • Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.zh-CN.vtt 1.8 kB
  • Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.zh-CN.vtt 1.8 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.en.vtt 1.8 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.ja.vtt 1.8 kB
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.th.vtt 1.8 kB
  • Part 04-Module 01-Lesson 01_Clustering/17. Feature Scaling Example--Axyt0bPCT0.pt-BR.vtt 1.8 kB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/22. Having-D4gmN0vnk58.pt-BR.vtt 1.8 kB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.en.vtt 1.8 kB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.zh-CN.vtt 1.8 kB
  • Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.pt-BR.vtt 1.8 kB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.en.vtt 1.8 kB
  • Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.zh-CN.vtt 1.8 kB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/12. L5 121 Lesson Summary V1-SOBCduyymkQ.en.vtt 1.8 kB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.pt-BR.vtt 1.8 kB
  • Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.pt-BR.vtt 1.8 kB
  • Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.pt-BR.vtt 1.8 kB
  • Part 04-Module 01-Lesson 01_Clustering/03. Two Types of Unsupervised Learning-aHK_rpaS_ts.pt-BR.vtt 1.8 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.zh-CN.vtt 1.8 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/27. Removing Data - Other Considerations-xrXk_Tvi0oQ.en.vtt 1.8 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/26. Removing Data - When Is It OK-oQhIPq5AccU.pt-BR.vtt 1.8 kB
  • Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.pt-BR.vtt 1.8 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt 1.8 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt 1.8 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.ja.vtt 1.8 kB
  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/01. Introduction-5DfFaAl1Wmc.pt-BR.vtt 1.8 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.zh-CN.vtt 1.8 kB
  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/04. Same Data Different Stories-jSSnkz3QT5Y.zh-CN.vtt 1.8 kB
  • Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.zh-CN.vtt 1.8 kB
  • Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.en.vtt 1.8 kB
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.en.vtt 1.8 kB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.en.vtt 1.8 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.en.vtt 1.8 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.zh-CN.vtt 1.8 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.it.vtt 1.8 kB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/19. DISTINCT-YDJEHkgKORY.ar.vtt 1.8 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.pt-BR.vtt 1.8 kB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt 1.8 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.zh-CN.vtt 1.8 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.zh-CN.vtt 1.8 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.zh-CN.vtt 1.8 kB
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/15. L2 10 Documentation V1 V3-M45B2VbPgjo.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.zh-CN.vtt 1.8 kB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.pt-BR.vtt 1.8 kB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/03. Fitting A Multiple Linear Regression Model-EZNvBF66_b0.en.vtt 1.8 kB
  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c03-practicalsignificance-01.png 1.8 kB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.ar.vtt 1.8 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/hidden-layer-weights.gif 1.8 kB
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  • Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.en.vtt 1.7 kB
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  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.zh-CN.vtt 1.7 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.pt-BR.vtt 1.7 kB
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  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.en.vtt 1.7 kB
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  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/04. Types Of Machine Learning - Supervised-Jn3xugBvs2U.en.vtt 1.7 kB
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  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.zh-CN.vtt 1.7 kB
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/20. Outro SC V1-YD1grQje9fw.en.vtt 1.7 kB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.pt-BR.vtt 1.7 kB
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.en.vtt 1.7 kB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.zh-CN.vtt 1.7 kB
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  • Part 08-Module 01-Lesson 06_Explanatory Visualizations/06. L6 061 Polishing Plots V3-4TixzVx79uk.pt-BR.vtt 1.7 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.pt-BR.vtt 1.7 kB
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  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.zh-CN.vtt 1.7 kB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.en.vtt 1.7 kB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.en.vtt 1.7 kB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.pt-BR.vtt 1.7 kB
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  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.zh-CN.vtt 1.7 kB
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.zh-CN.vtt 1.7 kB
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.zh-CN.vtt 1.7 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/26. Removing Data - When Is It OK-oQhIPq5AccU.en.vtt 1.7 kB
  • Part 04-Module 01-Lesson 01_Clustering/05. KMeans-B9jdQFpPEk0.en.vtt 1.7 kB
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  • Part 07-Module 01-Lesson 03_SQL Aggregations/04. COUNT-b4FCWAEGmLg.pt-BR.vtt 1.7 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt 1.7 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.zh-CN.vtt 1.7 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt 1.7 kB
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.ja.vtt 1.7 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.zh-CN.vtt 1.7 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.en.vtt 1.7 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.zh-CN.vtt 1.6 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.pt-BR.vtt 1.6 kB
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.ar.vtt 1.6 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.en.vtt 1.6 kB
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  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.pt-BR.vtt 1.6 kB
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  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.en.vtt 1.6 kB
  • Part 17-Module 02-Lesson 01_Concepts in Experiment Design/11. SMART Mnemonic-B0Bnxyu2aKM.en.vtt 1.6 kB
  • Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.zh-CN.vtt 1.6 kB
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  • Part 12-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.en.vtt 1.6 kB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.pt-BR.vtt 1.6 kB
  • Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.zh-CN.vtt 1.6 kB
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  • Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.pt-BR.vtt 1.6 kB
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.pt-BR.vtt 1.6 kB
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. L5 051 Faceting In Two Directions V3-lz5dcoTcV2o.en.vtt 1.6 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.es-ES.vtt 1.6 kB
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  • Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.zh-CN.vtt 1.6 kB
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  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.pt-BR.vtt 1.6 kB
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  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.pt-BR.vtt 1.6 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.en.vtt 1.6 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.en.vtt 1.6 kB
  • Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Git Pull In Theory-MjNU2LTDVAA.ar.vtt 1.6 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.ar.vtt 1.6 kB
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  • Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.pt-BR.vtt 1.5 kB
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  • Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.zh-CN.vtt 1.5 kB
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  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt 1.5 kB
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  • Part 16-Module 01-Lesson 02_ETL Pipelines/39. 47 Load V1 V1-Us1hWDaabxo.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.en.vtt 1.5 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.zh-CN.vtt 1.5 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt 1.5 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.pt-BR.vtt 1.5 kB
  • Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt 1.5 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.zh-CN.vtt 1.5 kB
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.ar.vtt 1.5 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.hr.vtt 1.5 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.pt-BR.vtt 1.5 kB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.zh-CN.vtt 1.5 kB
  • Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.pt-BR.vtt 1.5 kB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/13. L3 10 Captivate Your Audience Now What V1-Iy08sZYuqkI.en.vtt 1.5 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/03. Entity Relationship Diagrams-YY2TAJLEINA.ar.vtt 1.5 kB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.en.vtt 1.5 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.en.vtt 1.5 kB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/11. Captivate Your Audience - First Catch Their Eye-lO8-YKgW7y0.pt-BR.vtt 1.5 kB
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  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.en.vtt 1.5 kB
  • Part 04-Module 01-Lesson 04_PCA/17. When to Use PCA-arSP83-CM6w.pt-BR.vtt 1.5 kB
  • Part 15-Module 01-Lesson 01_Introduction to Software Engineering/02. L1 02 Course Overview V1 V4-v-DB0W_I2n8.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.en.vtt 1.5 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.en.vtt 1.5 kB
  • Part 16-Module 01-Lesson 03_NLP Pipelines/12. Part-of-Speech Tagging-WFEu8bXI5OA.zh-CN.vtt 1.5 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.pt-BR.vtt 1.5 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.pt-BR.vtt 1.5 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.en.vtt 1.5 kB
  • Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.zh-CN.vtt 1.5 kB
  • Part 15-Module 01-Lesson 06_Web Development/02. L4 Lesson Overview V2-9WQF-CCNdJ8.en.vtt 1.5 kB
  • Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.zh-CN.vtt 1.5 kB
  • Part 04-Module 01-Lesson 01_Clustering/16. Feature Scaling-rpTVp7C8AXo.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.en.vtt 1.5 kB
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.en.vtt 1.5 kB
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.zh-CN.vtt 1.5 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.en.vtt 1.5 kB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/03. What is the Difference-I3tQvrCgNrQ.pt-BR.vtt 1.5 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.ja.vtt 1.5 kB
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  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/15. Conclusions-3IFF1GzUq0Y.en.vtt 1.5 kB
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  • Part 03-Module 01-Lesson 03_Training Neural Networks/11. Vanishing Gradient-W_JJm_5syFw.en.vtt 1.5 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt 1.5 kB
  • Part 04-Module 01-Lesson 01_Clustering/04. 04 KMeans Use Cases 1 1 V2-25paySwVdAA.en.vtt 1.5 kB
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.en.vtt 1.5 kB
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.en.vtt 1.5 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt 1.5 kB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.ar.vtt 1.5 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt 1.5 kB
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  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/01. L2 01 Intro V1 V1-z7v7oa--W48.pt-BR.vtt 1.4 kB
  • Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt 1.4 kB
  • Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.zh-CN.vtt 1.4 kB
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  • Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.zh-CN.vtt 1.4 kB
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  • Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-O-4qRh74rkI.pt-BR.vtt 1.4 kB
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  • Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt 1.4 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.zh-CN.vtt 1.4 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.en.vtt 1.4 kB
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  • Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.pt-BR.vtt 1.4 kB
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.zh-CN.vtt 1.4 kB
  • Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt 1.4 kB
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  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. DataVis L3 11 V1-C8DGwJa_adA.pt-BR.vtt 1.4 kB
  • Part 04-Module 01-Lesson 04_PCA/04. Latent Features-kYLcVgpEwGs.pt-BR.vtt 1.4 kB
  • Part 15-Module 01-Lesson 06_Web Development/32. L4 Outro V2-8MyuJx5yu38.en.vtt 1.4 kB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.zh-CN.vtt 1.4 kB
  • Part 04-Module 01-Lesson 04_PCA/02. Lesson Topics-LBzA08F_r4w.pt-BR.vtt 1.4 kB
  • Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.zh-CN.vtt 1.4 kB
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.pt-BR.vtt 1.4 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.pt-BR.vtt 1.4 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.pt-BR.vtt 1.4 kB
  • Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt 1.4 kB
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.pt-BR.vtt 1.4 kB
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.en.vtt 1.4 kB
  • Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.pt-BR.vtt 1.4 kB
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.zh-CN.vtt 1.4 kB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.es-ES.vtt 1.4 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.en.vtt 1.4 kB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/11. Captivate Your Audience - First Catch Their Eye-lO8-YKgW7y0.en.vtt 1.4 kB
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.zh-CN.vtt 1.4 kB
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.zh-CN.vtt 1.4 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt 1.4 kB
  • Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.en.vtt 1.4 kB
  • Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.zh-CN.vtt 1.4 kB
  • Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.en.vtt 1.4 kB
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  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/02. First Things First-ehjC7JK-zMI.pt-BR.vtt 1.4 kB
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  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.en.vtt 1.4 kB
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  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.ar.vtt 1.4 kB
  • Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.en.vtt 1.4 kB
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.ja.vtt 1.4 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.ar.vtt 1.4 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/02. CRISP-DM-PaVwnGcqlSE.en.vtt 1.4 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.es-ES.vtt 1.4 kB
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.ar.vtt 1.3 kB
  • Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.en.vtt 1.3 kB
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  • Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.en.vtt 1.3 kB
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  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.en.vtt 1.3 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt 1.3 kB
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  • Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 1-SNFHYbJvlZU.en.vtt 1.3 kB
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  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.ar.vtt 1.2 kB
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  • Part 02-Module 01-Lesson 02_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.pt-BR.vtt 1.2 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.zh-CN.vtt 1.2 kB
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.pt-BR.vtt 1.2 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.it.vtt 1.2 kB
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  • Part 04-Module 01-Lesson 05_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.en.vtt 1.2 kB
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.en.vtt 1.2 kB
  • Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.pt-BR.vtt 1.2 kB
  • Part 07-Module 01-Lesson 03_SQL Aggregations/02. NULLs-WYUkLKn6XCw.pt-BR.vtt 1.2 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.en.vtt 1.2 kB
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.zh-CN.vtt 1.2 kB
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/09. What's Ahead-2Hxy2Jlu8nk.en.vtt 1.2 kB
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.en.vtt 1.2 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.th.vtt 1.2 kB
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  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.ar.vtt 1.2 kB
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/01. Introduction-TRw4bvZuEG8.pt-BR.vtt 1.2 kB
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.pt-BR.vtt 1.2 kB
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  • Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.zh-CN.vtt 1.2 kB
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  • Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-6LO6I5M18PQ.en.vtt 1.2 kB
  • Part 07-Module 01-Lesson 01_Basic SQL/07. How Databases Store Data-H0C9z_sRvLE.zh-CN.vtt 1.2 kB
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.en.vtt 1.2 kB
  • Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.en.vtt 1.2 kB
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt 1.2 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.ja.vtt 1.2 kB
  • Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.es-ES.vtt 1.2 kB
  • Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.zh-CN.vtt 1.2 kB
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.pt-BR.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/08. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt 1.2 kB
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.ar.vtt 1.2 kB
  • Part 20-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt 1.2 kB
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.zh-CN.vtt 1.1 kB
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.ar.vtt 1.1 kB
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  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.en.vtt 940 Bytes
  • Part 02-Module 01-Lesson 02_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.en.vtt 939 Bytes
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  • Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.pt-BR.vtt 935 Bytes
  • Part 10-Module 01-Lesson 03_Review a Repo's History/07. A Repository's History - Outro-9rUf2HbdAd8.zh-CN.vtt 933 Bytes
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  • Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.zh-CN.vtt 928 Bytes
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  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/19. Conclusion-_ATzG6khLdk.en.vtt 927 Bytes
  • Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.pt-BR.vtt 925 Bytes
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/01. 01 Intro-4C4PuJANIdE.zh-CN.vtt 922 Bytes
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/05. Machine Learning Workflow-0nA6oTIlwaA.en.vtt 921 Bytes
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  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.en.vtt 906 Bytes
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  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.pt-BR.vtt 899 Bytes
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  • Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.zh-CN.vtt 892 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.zh-CN.vtt 892 Bytes
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.zh-CN.vtt 890 Bytes
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.pt-BR.vtt 889 Bytes
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/05. Confusion-Matrix-Solution-ywwSzyU9rYs.pt-BR.vtt 889 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-8QEAYYIyopY.zh-CN.vtt 888 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.ja.vtt 887 Bytes
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.zh-CN.vtt 887 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.zh-CN.vtt 886 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.ar.vtt 886 Bytes
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  • Part 16-Module 01-Lesson 02_ETL Pipelines/05. 05 Extraction Idea 1 V1 V2-4dKG_08zMm4.en.vtt 885 Bytes
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  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.pt-BR.vtt 879 Bytes
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  • Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.pt-BR.vtt 874 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.ja.vtt 872 Bytes
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.pt-BR.vtt 871 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.es-ES.vtt 870 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.zh-CN.vtt 869 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.en-GB.vtt 868 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.pt-BR.vtt 867 Bytes
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.en.vtt 867 Bytes
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  • Part 17-Module 02-Lesson 03_AB Testing Case Study/14. Conclusion-2G6x3oQnjy4.en.vtt 865 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.pt-BR.vtt 864 Bytes
  • Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.zh-CN.vtt 862 Bytes
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.ar.vtt 861 Bytes
  • Part 08-Module 01-Lesson 07_Visualization Case Study/07. L7 0F1 Congrats V3-LF-obnL7CI0.pt-BR.vtt 859 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.en.vtt 856 Bytes
  • Part 14-Module 01-Lesson 02_Communicating to Stakeholders/10. Three Steps To Captivate Your Audience-BWS3oQYS-c4.en.vtt 855 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.hr.vtt 854 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/14. Performance Tuning Motivation-aY4_uYWEuoE.zh-CN.vtt 852 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.ar.vtt 851 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.ar.vtt 850 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.es-ES.vtt 850 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.pt-BR.vtt 848 Bytes
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.zh-CN.vtt 848 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.ar.vtt 847 Bytes
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/02. Identifying Recommendation Engines-KwegrgvV-V4.en.vtt 847 Bytes
  • Part 07-Module 01-Lesson 03_SQL Aggregations/09. MIN MAX-1ewVsgWUih8.pt-BR.vtt 847 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.ja.vtt 846 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.es-ES.vtt 845 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.ar.vtt 845 Bytes
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.pt-BR.vtt 844 Bytes
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color4.png 844 Bytes
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  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt 842 Bytes
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.pt-BR.vtt 841 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.ja.vtt 841 Bytes
  • Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.zh-CN.vtt 840 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt 840 Bytes
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color3.png 839 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.en.vtt 839 Bytes
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.ar.vtt 838 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.es-ES.vtt 837 Bytes
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  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.ar.vtt 837 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.en.vtt 836 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.en.vtt 836 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.ja.vtt 835 Bytes
  • Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.pt-BR.vtt 834 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.it.vtt 832 Bytes
  • Part 02-Module 01-Lesson 02_Linear Regression/17. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt 831 Bytes
  • Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.pt-BR.vtt 830 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.ar.vtt 829 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.pt-BR.vtt 829 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.zh-CN.vtt 828 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.ja.vtt 828 Bytes
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.en.vtt 828 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.en.vtt 826 Bytes
  • Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color5.png 826 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.en.vtt 826 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.pt-BR.vtt 825 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.en.vtt 824 Bytes
  • Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.en.vtt 824 Bytes
  • Part 07-Module 01-Lesson 03_SQL Aggregations/09. MIN MAX-1ewVsgWUih8.en.vtt 824 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.es-ES.vtt 823 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.ar.vtt 822 Bytes
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.en.vtt 821 Bytes
  • Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.pt-BR.vtt 820 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.ar.vtt 819 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.ja.vtt 819 Bytes
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.en.vtt 818 Bytes
  • Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.en.vtt 817 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.es-ES.vtt 817 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.zh-CN.vtt 817 Bytes
  • Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/25. L2 21 Conclusion V1 V1-anPnokWZOZQ.en.vtt 816 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.ar.vtt 816 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.ja.vtt 813 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.th.vtt 813 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt 812 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.zh-CN.vtt 812 Bytes
  • Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.pt-BR.vtt 810 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.ar.vtt 807 Bytes
  • Part 16-Module 01-Lesson 04_Machine Learning Pipelines/01. 01 Intro V1 V3-Zl_es7xtSqk.en.vtt 806 Bytes
  • Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.en.vtt 806 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.ja.vtt 805 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.en-GB.vtt 804 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.ar.vtt 802 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.pt-BR.vtt 802 Bytes
  • Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.pt-BR.vtt 800 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.en.vtt 799 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ar.vtt 799 Bytes
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.it.vtt 797 Bytes
  • Part 02-Module 01-Lesson 02_Linear Regression/17. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt 793 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.pt-BR.vtt 792 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.hr.vtt 792 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.en.vtt 790 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.en.vtt 790 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.en.vtt 789 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.zh-CN.vtt 787 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.ar.vtt 787 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.zh-CN.vtt 787 Bytes
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt 786 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.zh-CN.vtt 785 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.ar.vtt 783 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.en.vtt 782 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.en.vtt 781 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.pt-BR.vtt 780 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.zh-CN.vtt 780 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.th.vtt 779 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.th.vtt 777 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.es-ES.vtt 777 Bytes
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.zh-CN.vtt 775 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.es-ES.vtt 775 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.pt-BR.vtt 774 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.th.vtt 772 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.en.vtt 772 Bytes
  • Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt 771 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.pt-BR.vtt 769 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.pt-BR.vtt 769 Bytes
  • Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt 769 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.th.vtt 768 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.it.vtt 767 Bytes
  • Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.pt-BR.vtt 767 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pt-PT.vtt 765 Bytes
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.ar.vtt 765 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.it.vtt 764 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.zh-CN.vtt 763 Bytes
  • Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.en.vtt 763 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.ja.vtt 762 Bytes
  • Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.zh-CN.vtt 761 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.pt-BR.vtt 761 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.ja.vtt 760 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.zh-CN.vtt 759 Bytes
  • Part 08-Module 01-Lesson 07_Visualization Case Study/01. L7 011 Intro V1-Virihwp36do.pt-BR.vtt 759 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.es-ES.vtt 757 Bytes
  • Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.en.vtt 756 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.en.vtt 754 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.pt-BR.vtt 753 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.pt-BR.vtt 753 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.en.vtt 752 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.zh-CN.vtt 752 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.ar.vtt 752 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.zh-CN.vtt 751 Bytes
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.zh-CN.vtt 750 Bytes
  • Part 12-Module 01-Lesson 14_Regression/21. Recap-DzMi27LI5l4.zh-CN.vtt 750 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.hr.vtt 748 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.ja.vtt 748 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.es-ES.vtt 748 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.pt-BR.vtt 747 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.ar.vtt 747 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.es-ES.vtt 746 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.es-ES.vtt 746 Bytes
  • Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.pt-BR.vtt 746 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.zh-CN.vtt 745 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.pt-BR.vtt 745 Bytes
  • Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.zh-CN.vtt 745 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.it.vtt 742 Bytes
  • Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.zh-CN.vtt 742 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.zh-CN.vtt 741 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.es-ES.vtt 741 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.pt-BR.vtt 741 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.en.vtt 738 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.ar.vtt 738 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.pt-BR.vtt 737 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.pt-BR.vtt 737 Bytes
  • Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.en.vtt 737 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.zh-CN.vtt 736 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.en.vtt 736 Bytes
  • Part 12-Module 01-Lesson 15_Multiple Linear Regression/26. Recap-VM6GGNC2q8I.zh-CN.vtt 736 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.pt-BR.vtt 736 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.pt-BR.vtt 735 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.pt-BR.vtt 735 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.en.vtt 734 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.ar.vtt 731 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.ar.vtt 731 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.ja.vtt 730 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.th.vtt 729 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.es-ES.vtt 729 Bytes
  • Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/17. Funk SVD Review-nc3GMIrISHE.en.vtt 729 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.es-ES.vtt 728 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.pt-BR.vtt 728 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.ar.vtt 727 Bytes
  • Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt 727 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.ar.vtt 726 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.en.vtt 725 Bytes
  • Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/04. Intro To MovieTweetings-cuXvLIkq_W8.en.vtt 725 Bytes
  • Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.zh-CN.vtt 724 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.th.vtt 723 Bytes
  • Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt 723 Bytes
  • Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.en.vtt 720 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.ar.vtt 717 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.ja.vtt 717 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.th.vtt 717 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.ja.vtt 716 Bytes
  • Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt 716 Bytes
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.en-US.vtt 716 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.en.vtt 715 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.en.vtt 715 Bytes
  • Part 07-Module 01-Lesson 03_SQL Aggregations/09. MIN MAX-1ewVsgWUih8.zh-CN.vtt 715 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pl.vtt 715 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.en.vtt 714 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.zh-CN.vtt 713 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.it.vtt 712 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.ja.vtt 710 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.ja.vtt 708 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.zh-CN.vtt 708 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.zh-CN.vtt 707 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ar.vtt 706 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt 705 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.pt-BR.vtt 705 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.en.vtt 704 Bytes
  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.zh-CN.vtt 704 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.pt-BR.vtt 702 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.it.vtt 702 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.zh-CN.vtt 701 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt 701 Bytes
  • Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.en.vtt 700 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.th.vtt 700 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.pt-BR.vtt 699 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.zh-CN.vtt 698 Bytes
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.it.vtt 697 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.pt-BR.vtt 696 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.pt-BR.vtt 695 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.it.vtt 695 Bytes
  • Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.zh-CN.vtt 695 Bytes
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.pt-BR.vtt 695 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.pt-BR.vtt 694 Bytes
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.en.vtt 694 Bytes
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.pt-BR.vtt 694 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.pt-BR.vtt 692 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.pt-BR.vtt 692 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.pt-BR.vtt 691 Bytes
  • Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt 690 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.en.vtt 690 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.hr.vtt 690 Bytes
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.en.vtt 688 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.pt-BR.vtt 688 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.pt-BR.vtt 687 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.es-ES.vtt 686 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.pt-BR.vtt 686 Bytes
  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.en.vtt 685 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.ar.vtt 684 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.ar.vtt 684 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.ar.vtt 684 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pt-BR.vtt 684 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.ar.vtt 683 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.es-ES.vtt 682 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.en.vtt 682 Bytes
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.en.vtt 682 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.en.vtt 682 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.es-ES.vtt 681 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.pt-BR.vtt 680 Bytes
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.ar.vtt 679 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.es-ES.vtt 679 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.es-ES.vtt 679 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.th.vtt 677 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.zh-CN.vtt 676 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.zh-CN.vtt 675 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.en.vtt 675 Bytes
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  • Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.ar.vtt 673 Bytes
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  • Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.en.vtt 671 Bytes
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  • Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.zh-CN.vtt 670 Bytes
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.es-ES.vtt 665 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.ar.vtt 665 Bytes
  • Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.zh-CN.vtt 664 Bytes
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.hr.vtt 662 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.en.vtt 658 Bytes
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  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.pt.vtt 656 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.es-ES.vtt 656 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt 655 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.en.vtt 655 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.en.vtt 655 Bytes
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.pt-BR.vtt 653 Bytes
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.en.vtt 653 Bytes
  • Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.ar.vtt 652 Bytes
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  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.en.vtt 651 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.ar.vtt 650 Bytes
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  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.en.vtt 650 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.en.vtt 646 Bytes
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  • Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.zh-CN.vtt 643 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.zh-CN.vtt 643 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.pt-BR.vtt 642 Bytes
  • Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.en.vtt 641 Bytes
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.hr.vtt 640 Bytes
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  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.zh-CN.vtt 639 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.en.vtt 639 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt 638 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.zh-CN.vtt 638 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.hr.vtt 637 Bytes
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  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt 635 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.th.vtt 634 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.zh-CN.vtt 633 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.en.vtt 633 Bytes
  • Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt 631 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.en.vtt 630 Bytes
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.pt-BR.vtt 629 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.es-ES.vtt 629 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.zh-CN.vtt 629 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.en.vtt 628 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.ja.vtt 627 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.ar.vtt 626 Bytes
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.it.vtt 625 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.ja.vtt 625 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.ar.vtt 623 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.ja.vtt 623 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.en.vtt 622 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.zh-CN.vtt 622 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.ja.vtt 621 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.pt-BR.vtt 620 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.pt-BR.vtt 620 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.it.vtt 618 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.pt-BR.vtt 618 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.ja.vtt 618 Bytes
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt 618 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ru.vtt 617 Bytes
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.en.vtt 617 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ja.vtt 617 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.zh-CN.vtt 617 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.en-US.vtt 617 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.en.vtt 617 Bytes
  • Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.pt-BR.vtt 617 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.zh-CN.vtt 616 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.zh-CN.vtt 616 Bytes
  • Part 10-Module 01-Lesson 07_Working With Remotes/04. L1 - Git Push In Theory-21TvMEtMRys.zh-CN.vtt 616 Bytes
  • Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.zh-CN.vtt 616 Bytes
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt 615 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.ar.vtt 615 Bytes
  • Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.zh-CN.vtt 613 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.es-ES.vtt 611 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.en.vtt 610 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.zh-CN.vtt 609 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.ja.vtt 608 Bytes
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.es-ES.vtt 608 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.ar.vtt 607 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.th.vtt 604 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.th.vtt 603 Bytes
  • Part 01-Module 03-Lesson 01_Setting Up Your Computer/20. L2 02 Outro REPLACEMENT-W-6Se0G_FVE.en.vtt 603 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.pt-BR.vtt 602 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.en.vtt 599 Bytes
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.pt-BR.vtt 599 Bytes
  • Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt 599 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.hr.vtt 599 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.en.vtt 598 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.pt-BR.vtt 597 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.ar.vtt 591 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.ja.vtt 589 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.pt-BR.vtt 589 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt 588 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.ja.vtt 587 Bytes
  • Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.en.vtt 586 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.en.vtt 586 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt 586 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.ja.vtt 584 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.ar.vtt 584 Bytes
  • Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.pt-BR.vtt 583 Bytes
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.pt-BR.vtt 582 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.ar.vtt 582 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.zh-CN.vtt 582 Bytes
  • Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.zh-CN.vtt 582 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.pt-BR.vtt 579 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.th.vtt 579 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-Hans.vtt 579 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.zh-CN.vtt 579 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.th.vtt 578 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.pt-BR.vtt 576 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.es-ES.vtt 575 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.en.vtt 575 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt 574 Bytes
  • Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.pt-BR.vtt 574 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.ja.vtt 572 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.en.vtt 572 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.zh-CN.vtt 572 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.it.vtt 571 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.en.vtt 571 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.pt-BR.vtt 570 Bytes
  • Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.zh-CN.vtt 569 Bytes
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.en.vtt 569 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.pt-BR.vtt 567 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.pt-BR.vtt 567 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.zh-CN.vtt 566 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.es-ES.vtt 566 Bytes
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.es-ES.vtt 565 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.en.vtt 564 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.pt-BR.vtt 563 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ar.vtt 562 Bytes
  • Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.zh-CN.vtt 561 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.es-ES.vtt 560 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.ar.vtt 558 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.en.vtt 558 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.ja.vtt 557 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-CN.vtt 556 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.en.vtt 555 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.zh-CN.vtt 554 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.th.vtt 554 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.zh-CN.vtt 553 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.en.vtt 553 Bytes
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.pt-BR.vtt 552 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.zh-CN.vtt 552 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.zh-CN.vtt 549 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ar.vtt 549 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.en.vtt 549 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.th.vtt 548 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.pt-BR.vtt 547 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.zh-CN.vtt 547 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.ar.vtt 547 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.pt-BR.vtt 547 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ar.vtt 544 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt 543 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.pt-BR.vtt 541 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.th.vtt 541 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.ja.vtt 541 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt 540 Bytes
  • Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.zh-CN.vtt 540 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.en.vtt 540 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.hr.vtt 539 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.es-ES.vtt 538 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.zh-CN.vtt 538 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.en.vtt 537 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.zh-CN.vtt 536 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.en.vtt 536 Bytes
  • Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.zh-CN.vtt 536 Bytes
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.es-ES.vtt 535 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.ar.vtt 535 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.en.vtt 534 Bytes
  • Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.ar.vtt 534 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.pt-BR.vtt 533 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.es-ES.vtt 533 Bytes
  • Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt 533 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ar.vtt 533 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.ar.vtt 532 Bytes
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.ja.vtt 532 Bytes
  • Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.zh-CN.vtt 532 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.zh-CN.vtt 532 Bytes
  • Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.zh-CN.vtt 531 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ja.vtt 531 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.es-ES.vtt 531 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.en.vtt 530 Bytes
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.pt-BR.vtt 530 Bytes
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.hr.vtt 530 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.pt-BR.vtt 529 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.es-ES.vtt 529 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.en.vtt 528 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.ja.vtt 527 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.en.vtt 527 Bytes
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Projects-1-E_ZYovKeI.en.vtt 527 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.pt-BR.vtt 526 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.ja.vtt 525 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ar.vtt 524 Bytes
  • Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt 524 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.en.vtt 524 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.pt-BR.vtt 523 Bytes
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.en.vtt 523 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.pt-BR.vtt 523 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.zh-CN.vtt 522 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.zh-CN.vtt 522 Bytes
  • Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.pt-BR.vtt 522 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.zh-CN.vtt 521 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.ar.vtt 518 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ar.vtt 518 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt 517 Bytes
  • Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.hr.vtt 517 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ar.vtt 516 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.pt-BR.vtt 516 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.es-ES.vtt 516 Bytes
  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Projects-1-E_ZYovKeI.pt-BR.vtt 516 Bytes
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.zh-CN.vtt 516 Bytes
  • Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.en.vtt 514 Bytes
  • Part 16-Module 01-Lesson 02_ETL Pipelines/41. 52 Putting It All Together V1 1 V1-D2Th0KdPI-Y.pt-BR.vtt 514 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt 514 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.en.vtt 513 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.ar.vtt 509 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.en.vtt 508 Bytes
  • Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.pt-BR.vtt 508 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.zh-CN.vtt 505 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.it.vtt 504 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.zh-CN.vtt 504 Bytes
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.pt-BR.vtt 503 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.zh-CN.vtt 503 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.es-ES.vtt 502 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.en.vtt 501 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.pt-BR.vtt 501 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.pt-BR.vtt 500 Bytes
  • Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.zh-CN.vtt 499 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.pt-BR.vtt 498 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.pt-BR.vtt 497 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.en.vtt 497 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.ar.vtt 497 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.pt-BR.vtt 496 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.en.vtt 496 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.pt-BR.vtt 495 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 Bytes
  • Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.zh-CN.vtt 495 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 Bytes
  • Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.en.vtt 495 Bytes
  • Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/05. Outro-dVrYQ7o8a-k.en.vtt 493 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.ar.vtt 493 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.es-ES.vtt 493 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.ja.vtt 492 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.zh-CN.vtt 492 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.ja.vtt 491 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.en.vtt 491 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.ar.vtt 491 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.zh-CN.vtt 490 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt 490 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.pt-BR.vtt 488 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.en.vtt 486 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.en.vtt 484 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.ja.vtt 483 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.en-US.vtt 482 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.pt-BR.vtt 482 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.pt-BR.vtt 481 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ar.vtt 481 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.th.vtt 480 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.es-ES.vtt 480 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.en.vtt 479 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.es-ES.vtt 478 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.pt-BR.vtt 478 Bytes
  • Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.it.vtt 476 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.en.vtt 476 Bytes
  • Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.en.vtt 473 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.zh-CN.vtt 473 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.pt-BR.vtt 473 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.ja.vtt 473 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.es-ES.vtt 473 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.ar.vtt 473 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.ar.vtt 473 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.pt-BR.vtt 473 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt 473 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.ar.vtt 472 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.en-US.vtt 472 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.ar.vtt 471 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.es-ES.vtt 471 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.es-ES.vtt 471 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.en.vtt 470 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.en.vtt 469 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.hr.vtt 469 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.en.vtt 468 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.en.vtt 468 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.zh-CN.vtt 467 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt 467 Bytes
  • Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.en.vtt 467 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.es-ES.vtt 466 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt 466 Bytes
  • Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.en.vtt 466 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt 465 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.pt-BR.vtt 465 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.ar.vtt 463 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.ar.vtt 462 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.en.vtt 461 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.zh-CN.vtt 460 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.zh-CN.vtt 460 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.es-ES.vtt 459 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.ar.vtt 459 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ja.vtt 458 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.zh-CN.vtt 458 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt 457 Bytes
  • Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.zh-CN.vtt 456 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.ar.vtt 456 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.it.vtt 456 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.zh-CN.vtt 456 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.en.vtt 455 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.ar.vtt 455 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.es-ES.vtt 455 Bytes
  • README.txt 454 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.en.vtt 453 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.hr.vtt 453 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.it.vtt 452 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.es-ES.vtt 451 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.zh-CN.vtt 451 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.en.vtt 449 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.ja.vtt 449 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.th.vtt 447 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.pt-BR.vtt 447 Bytes
  • Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.zh-CN.vtt 446 Bytes
  • Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.pt-BR.vtt 445 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ar.vtt 445 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.pt-BR.vtt 444 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.it.vtt 444 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.ja.vtt 444 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.pt-BR.vtt 442 Bytes
  • Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.zh-CN.vtt 442 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.th.vtt 441 Bytes
  • Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.zh-CN.vtt 441 Bytes
  • Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.zh-CN.vtt 440 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.pt-BR.vtt 440 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ar.vtt 437 Bytes
  • Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt 437 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ar.vtt 436 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.it.vtt 435 Bytes
  • Part 16-Module 01-Lesson 02_ETL Pipelines/41. 52 Putting It All Together V1 1 V1-D2Th0KdPI-Y.en.vtt 434 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.it.vtt 433 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.en.vtt 433 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.pt-BR.vtt 433 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.en.vtt 432 Bytes
  • Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt 432 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.zh-CN.vtt 431 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.en.vtt 428 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.ja.vtt 428 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.pt-BR.vtt 427 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.en.vtt 427 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.zh-CN.vtt 426 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.es-ES.vtt 426 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.es-ES.vtt 425 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.zh-CN.vtt 425 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.hr.vtt 425 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.en.vtt 425 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.en.vtt 424 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.es-ES.vtt 424 Bytes
  • Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.en.vtt 423 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.th.vtt 423 Bytes
  • Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/05. Outro-dVrYQ7o8a-k.pt-BR.vtt 422 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.pt-BR.vtt 422 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.it.vtt 422 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.ar.vtt 422 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.zh-CN.vtt 422 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.ja.vtt 420 Bytes
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.zh-CN.vtt 420 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ar.vtt 420 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 Bytes
  • Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.zh-CN.vtt 419 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.ja.vtt 419 Bytes
  • Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.zh-CN.vtt 419 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.pt-BR.vtt 418 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.ja.vtt 417 Bytes
  • Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.en.vtt 417 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.es-ES.vtt 417 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.en.vtt 416 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.th.vtt 415 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.ja.vtt 415 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.it.vtt 414 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.en.vtt 414 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.es-ES.vtt 412 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.zh-CN.vtt 411 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.ar.vtt 411 Bytes
  • Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.pt-BR.vtt 411 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.pt-BR.vtt 410 Bytes
  • Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.zh-CN.vtt 410 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.pt-BR.vtt 410 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.it.vtt 409 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.zh-CN.vtt 408 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.zh-CN.vtt 408 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.ar.vtt 408 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.es-ES.vtt 406 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.pt-BR.vtt 406 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ar.vtt 405 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.pt-BR.vtt 405 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.ar.vtt 404 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.ja.vtt 403 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.zh-CN.vtt 403 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.zh-CN.vtt 403 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.ar.vtt 402 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.pt-BR.vtt 401 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.ar.vtt 399 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.zh-CN.vtt 399 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.en.vtt 399 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ar.vtt 399 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.th.vtt 398 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.ja.vtt 397 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.pt-BR.vtt 397 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.pt-BR.vtt 396 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ar.vtt 396 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.ar.vtt 395 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ja.vtt 395 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.en.vtt 394 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.pt-BR.vtt 393 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.th.vtt 393 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.es-ES.vtt 393 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.pt-BR.vtt 391 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ja.vtt 390 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.es-ES.vtt 390 Bytes
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.ja.vtt 389 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.ja.vtt 389 Bytes
  • Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.zh-CN.vtt 389 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.es-ES.vtt 389 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.zh-CN.vtt 389 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.es-ES.vtt 388 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.en.vtt 387 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.ar.vtt 387 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.hr.vtt 384 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.hr.vtt 383 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.pt-BR.vtt 383 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.en.vtt 382 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.it.vtt 382 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.zh-CN.vtt 380 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.zh-CN.vtt 380 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.zh-CN.vtt 379 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.zh-CN.vtt 378 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.pt-BR.vtt 377 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.pt-BR.vtt 377 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.zh-CN.vtt 376 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.pt-BR.vtt 376 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.it.vtt 375 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.pt-BR.vtt 375 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ja.vtt 374 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.pt-BR.vtt 373 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.ja.vtt 373 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.en.vtt 373 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.it.vtt 372 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ja.vtt 371 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.en.vtt 370 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ja.vtt 370 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.zh-CN.vtt 369 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.zh-CN.vtt 369 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.es-ES.vtt 367 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.es-ES.vtt 366 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.zh-CN.vtt 366 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ar.vtt 364 Bytes
  • Part 20-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.en.vtt 364 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.pt-BR.vtt 362 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.zh-CN.vtt 362 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.es-ES.vtt 360 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.pt-BR.vtt 360 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.it.vtt 359 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.th.vtt 359 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.es-ES.vtt 358 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.en.vtt 358 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ja.vtt 357 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.es-ES.vtt 357 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.en.vtt 357 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.es-ES.vtt 357 Bytes
  • Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.es-ES.vtt 356 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.pt-BR.vtt 356 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.zh-CN.vtt 355 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.es-ES.vtt 355 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.zh-CN.vtt 355 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.en.vtt 354 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.pt-BR.vtt 354 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.th.vtt 354 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.pt-BR.vtt 354 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.en.vtt 353 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.ja.vtt 353 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.zh-CN.vtt 352 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.ar.vtt 352 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.en.vtt 351 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.zh-CN.vtt 351 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.en.vtt 350 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ja.vtt 350 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.es-ES.vtt 350 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.en.vtt 349 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.en.vtt 349 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.pt-BR.vtt 349 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.es-ES.vtt 348 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.es-ES.vtt 348 Bytes
  • Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.zh-CN.vtt 348 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.en.vtt 346 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.ja.vtt 345 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.pt-BR.vtt 344 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.en.vtt 343 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.th.vtt 342 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.en.vtt 342 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.en.vtt 342 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.ar.vtt 342 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.zh-CN.vtt 341 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.en.vtt 341 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.it.vtt 341 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.ar.vtt 341 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.en.vtt 340 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.ar.vtt 339 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.th.vtt 339 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.en.vtt 339 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.pt-BR.vtt 339 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.it.vtt 338 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.es-ES.vtt 337 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.en.vtt 337 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.pt-BR.vtt 337 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ar.vtt 336 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.en.vtt 336 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.pt-BR.vtt 335 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.zh-CN.vtt 335 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.ar.vtt 334 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.es-ES.vtt 333 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.zh-CN.vtt 333 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ja.vtt 332 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.es-ES.vtt 332 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.pt-BR.vtt 332 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.zh-CN.vtt 331 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ja.vtt 331 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.es-ES.vtt 329 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.pt-BR.vtt 329 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.ja.vtt 327 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.es-ES.vtt 327 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.ja.vtt 326 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.ar.vtt 326 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.ja.vtt 326 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ar.vtt 325 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.en.vtt 325 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.zh-CN.vtt 325 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.it.vtt 324 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.zh-CN.vtt 323 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.ja.vtt 323 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.en.vtt 322 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.zh-CN.vtt 320 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.en.vtt 319 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.ar.vtt 319 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.ar.vtt 319 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.hr.vtt 319 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.es-ES.vtt 318 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.pt-BR.vtt 317 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.en.vtt 317 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.zh-CN.vtt 316 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.it.vtt 315 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ja.vtt 315 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ar.vtt 315 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.zh-CN.vtt 314 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.it.vtt 314 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.en.vtt 313 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.ar.vtt 312 Bytes
  • Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.hr.vtt 312 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.it.vtt 311 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.en.vtt 310 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.ja.vtt 310 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.pt-BR.vtt 309 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.zh-CN.vtt 309 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.it.vtt 308 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.it.vtt 307 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.pt-BR.vtt 307 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.en.vtt 307 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.zh-CN.vtt 307 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.en.vtt 306 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.pt-BR.vtt 305 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.zh-CN.vtt 305 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ja.vtt 305 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.hr.vtt 305 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.th.vtt 305 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ja.vtt 305 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.ja.vtt 304 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.zh-CN.vtt 304 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.ja.vtt 304 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.zh-CN.vtt 303 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.zh-CN.vtt 303 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.ar.vtt 303 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.ar.vtt 303 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.zh-CN.vtt 302 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.ja.vtt 302 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.ar.vtt 302 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.en.vtt 302 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.ar.vtt 302 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ja.vtt 301 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.es-ES.vtt 301 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.ja.vtt 301 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.ja.vtt 300 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.ja.vtt 300 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.th.vtt 299 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.zh-CN.vtt 298 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.ja.vtt 298 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.zh-CN.vtt 297 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.zh-CN.vtt 296 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ja.vtt 296 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.zh-CN.vtt 294 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ja.vtt 293 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.pt-BR.vtt 293 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.zh-CN.vtt 293 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.en.vtt 292 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.zh-CN.vtt 291 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.pt-BR.vtt 291 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.it.vtt 291 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.hr.vtt 291 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.hr.vtt 289 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.it.vtt 289 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.zh-CN.vtt 288 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.ar.vtt 286 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.pt-BR.vtt 285 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.pt-BR.vtt 284 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.ar.vtt 283 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.zh-CN.vtt 283 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.zh-CN.vtt 282 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.es-ES.vtt 281 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.pt-BR.vtt 281 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.en.vtt 281 Bytes
  • Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.zh-CN.vtt 280 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.ar.vtt 279 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.en.vtt 279 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.ar.vtt 278 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.zh-CN.vtt 277 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.en.vtt 277 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.pt-BR.vtt 276 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.zh-CN.vtt 275 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.es-ES.vtt 272 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.pt-BR.vtt 269 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.en.vtt 268 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.zh-CN.vtt 268 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.th.vtt 267 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.ar.vtt 265 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.ar.vtt 265 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.pt-BR.vtt 265 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.ar.vtt 265 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.ja.vtt 264 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.pt-BR.vtt 264 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ja.vtt 262 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.pt-BR.vtt 262 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.ja.vtt 261 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.en.vtt 260 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.es-ES.vtt 260 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.ja.vtt 260 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.pt-BR.vtt 260 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.es-ES.vtt 258 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.es-ES.vtt 257 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.zh-CN.vtt 257 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.zh-CN.vtt 256 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.en.vtt 256 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.en.vtt 256 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ja.vtt 254 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.ar.vtt 254 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.pt-BR.vtt 254 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.pt-BR.vtt 253 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.es-ES.vtt 252 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.en.vtt 252 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.ar.vtt 252 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.zh-CN.vtt 251 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-T2dveKB64Ho.ar.vtt 248 Bytes
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  • Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.en.vtt 246 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.en.vtt 246 Bytes
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  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.zh-CN.vtt 241 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.zh-CN.vtt 241 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.th.vtt 240 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.it.vtt 240 Bytes
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  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.pt-BR.vtt 240 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.th.vtt 240 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.it.vtt 239 Bytes
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  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.ar.vtt 238 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.hr.vtt 238 Bytes
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  • Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.ja.vtt 237 Bytes
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.es-ES.vtt 236 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.it.vtt 234 Bytes
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  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.ar.vtt 234 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.es-ES.vtt 234 Bytes
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.zh-CN.vtt 232 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.en.vtt 231 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.es-ES.vtt 231 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.zh-CN.vtt 228 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.th.vtt 228 Bytes
  • Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.zh-CN.vtt 228 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.es-ES.vtt 227 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.zh-CN.vtt 226 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.zh-CN.vtt 225 Bytes
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.ar.vtt 222 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.ar.vtt 222 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.en.vtt 222 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.ar.vtt 222 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.es-ES.vtt 221 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.es-ES.vtt 219 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.ja.vtt 219 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.pt-BR.vtt 219 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.en.vtt 219 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.en.vtt 219 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.zh-CN.vtt 218 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.ar.vtt 218 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.pt-BR.vtt 218 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.ar.vtt 218 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.en.vtt 217 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.ja.vtt 217 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.es-ES.vtt 217 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.pt-BR.vtt 216 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.ja.vtt 216 Bytes
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.zh-CN.vtt 216 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.en.vtt 216 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.pt-PT.vtt 215 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.zh-CN.vtt 215 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.en.vtt 214 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.zh-CN.vtt 214 Bytes
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  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.en.vtt 214 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.hr.vtt 213 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.en.vtt 213 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.it.vtt 213 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.pt-BR.vtt 212 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.zh-CN.vtt 212 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.ja.vtt 212 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.it.vtt 212 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.zh-CN.vtt 212 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.ar.vtt 211 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.en.vtt 210 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.it.vtt 210 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.zh-CN.vtt 210 Bytes
  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.es-ES.vtt 210 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.hr.vtt 209 Bytes
  • Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.pt-BR.vtt 209 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.ar.vtt 208 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.ar.vtt 207 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.zh-CN.vtt 207 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.th.vtt 205 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.hr.vtt 204 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.ar.vtt 204 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.th.vtt 204 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.zh-CN.vtt 204 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.pt-BR.vtt 203 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.ar.vtt 202 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.ja.vtt 202 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.pt-BR.vtt 202 Bytes
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  • Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ja.vtt 200 Bytes
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  • Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.ja.vtt 198 Bytes
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  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.ja.vtt 197 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.zh-CN.vtt 197 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.hr.vtt 197 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.en.vtt 196 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.ar.vtt 196 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.zh-CN.vtt 196 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.zh-CN.vtt 192 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.es-ES.vtt 192 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.ar.vtt 191 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.ar.vtt 190 Bytes
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  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.en.vtt 190 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.zh-Hans.vtt 189 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.pt-BR.vtt 188 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.it.vtt 187 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.zh-CN.vtt 187 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.ja.vtt 187 Bytes
  • Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.pt-BR.vtt 186 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.zh-CN.vtt 186 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.en.vtt 186 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.es-ES.vtt 185 Bytes
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  • Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.ar.vtt 184 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.ja.vtt 184 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.pt-BR.vtt 183 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.zh-CN.vtt 182 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.en.vtt 182 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.zh-CN.vtt 180 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.zh-CN.vtt 180 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.en.vtt 180 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.ja.vtt 180 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-T2dveKB64Ho.en.vtt 178 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.th.vtt 178 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.it.vtt 178 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.pt-BR.vtt 177 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.th.vtt 177 Bytes
  • Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.zh-CN.vtt 177 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.ar.vtt 176 Bytes
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  • Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.it.vtt 174 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.es-ES.vtt 174 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.es-ES.vtt 174 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.en.vtt 174 Bytes
  • Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.pt-BR.vtt 173 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.pt-BR.vtt 173 Bytes
  • Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.it.vtt 173 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.pt-BR.vtt 173 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.it.vtt 171 Bytes
  • Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.it.vtt 171 Bytes
  • Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.pt-BR.vtt 171 Bytes
  • Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.pt-BR.vtt 170 Bytes
  • Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.es-ES.vtt 170 Bytes
  • Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.pt-BR.vtt 169 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.pt-BR.vtt 169 Bytes
  • Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.en.vtt 169 Bytes
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