[GigaCourse.Com] Udemy - Python for Data Science & Machine Learning from A-Z

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[GigaCourse.Com] Udemy - Python for Data Science & Machine Learning from A-Z

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收录时间：2022-01-09
最近下载：2024-11-25

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文件列表

  • 19. PCA/7. PCA - Image Compression.mp4 262.1 MB
  • 16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.mp4 212.4 MB
  • 15. Decision Trees/7. ID3 - Putting Everything Together.mp4 191.3 MB
  • 14. K Nearest Neighbors/3. EDA on Iris Dataset.mp4 169.7 MB
  • 15. Decision Trees/3. What is Entropy and Information Gain.mp4 142.7 MB
  • 19. PCA/9. PCA - Biplot and the Screen Plot.mp4 142.2 MB
  • 1. Introduction/6. How To Get a Data Science Job.mp4 137.6 MB
  • 1. Introduction/5. What is a Data Scientist.mp4 133.7 MB
  • 17. Support Vector Machines/6. SVM - Kernel Types.mp4 132.5 MB
  • 15. Decision Trees/2. EDA on Adult Dataset.mp4 129.2 MB
  • 15. Decision Trees/8. Evaluating our ID3 implementation.mp4 127.9 MB
  • 19. PCA/8. PCA Data Preprocessing.mp4 126.3 MB
  • 15. Decision Trees/13. Pruning.mp4 118.5 MB
  • 17. Support Vector Machines/8. SVM with Non-linear Dataset.mp4 117.0 MB
  • 13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.mp4 115.7 MB
  • 18. K-means/3. Representing Clusters.mp4 114.9 MB
  • 3. Python For Data Science/15. Python Dictionaries.mp4 109.2 MB
  • 17. Support Vector Machines/7. SVM with Linear Dataset (Iris).mp4 106.5 MB
  • 18. K-means/1. Unsupervised Machine Learning Intro.mp4 105.8 MB
  • 9. Machine Learning/1. Introduction To Machine Learning.mp4 103.5 MB
  • 16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.mp4 96.4 MB
  • 14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.mp4 94.7 MB
  • 2. Data Science & Machine Learning Concepts/2. What is Data Science.mp4 92.3 MB
  • 14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.mp4 91.2 MB
  • 3. Python For Data Science/7. Python Operators.mp4 91.0 MB
  • 16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.mp4 90.1 MB
  • 15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 89.4 MB
  • 2. Data Science & Machine Learning Concepts/3. What is Machine Learning.mp4 87.5 MB
  • 18. K-means/2. Unsupervised Machine Learning Continued.mp4 87.2 MB
  • 15. Decision Trees/12. Decision Trees Hyper-parameters.mp4 85.2 MB
  • 1. Introduction/4. Data Science Job Roles.mp4 83.7 MB
  • 1. Introduction/7. Data Science Projects Overview.mp4 83.3 MB
  • 2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.mp4 81.8 MB
  • 2. Data Science & Machine Learning Concepts/5. What is Deep Learning.mp4 81.6 MB
  • 17. Support Vector Machines/5. Kernel Trick.mp4 80.8 MB
  • 2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.mp4 79.6 MB
  • 8. Python Data Visualization/1. Data Visualization Overview.mp4 76.6 MB
  • 7. Pandas Data Analysis/2. Introduction to Pandas Continued.mp4 74.5 MB
  • 3. Python For Data Science/19. Object Oriented Programming in Python.mp4 73.7 MB
  • 19. PCA/10. PCA - Feature Scaling and Screen Plot.mp4 71.5 MB
  • 15. Decision Trees/10. Visualizing the tree.mp4 71.5 MB
  • 19. PCA/12. PCA - Visualization.mp4 71.3 MB
  • 17. Support Vector Machines/3. Hard vs Soft Margins.mp4 68.8 MB
  • 15. Decision Trees/9. Compare with Sklearn implementation.mp4 68.8 MB
  • 15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 67.1 MB
  • 3. Python For Data Science/18. Python Functions.mp4 65.5 MB
  • 5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.mp4 63.5 MB
  • 3. Python For Data Science/13. More about Lists.mp4 63.4 MB
  • 19. PCA/4. PCA Algorithm Steps (Mathematics).mp4 60.5 MB
  • 3. Python For Data Science/9. Python Strings.mp4 59.0 MB
  • 16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.mp4 58.6 MB
  • 3. Python For Data Science/10. Python Conditional Statements.mp4 57.3 MB
  • 3. Python For Data Science/14. Python Tuples.mp4 57.2 MB
  • 10. Data Loading & Exploration/1. Exploratory Data Analysis.mp4 53.0 MB
  • 16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.mp4 53.0 MB
  • 14. K Nearest Neighbors/10. Feature scaling in KNN.mp4 51.8 MB
  • 17. Support Vector Machines/2. SVM intuition.mp4 51.2 MB
  • 15. Decision Trees/15. Decision Trees Pros and Cons.mp4 50.1 MB
  • 19. PCA/2. What is PCA.mp4 49.6 MB
  • 3. Python For Data Science/17. Compound Data Types & When to use each one.mp4 49.4 MB
  • 1. Introduction/2. Data Science + Machine Learning Marketplace.mp4 49.2 MB
  • 7. Pandas Data Analysis/1. Introduction to Pandas.mp4 49.1 MB
  • 14. K Nearest Neighbors/11. Curse of dimensionality.mp4 48.2 MB
  • 4. Statistics for Data Science/6. Inferential Statistics.mp4 47.2 MB
  • 16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).mp4 44.1 MB
  • 6. NumPy Data Analysis/3. NumPy Arrays Basics.mp4 41.9 MB
  • 16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.mp4 41.6 MB
  • 17. Support Vector Machines/10. SMV - Project Overview.mp4 41.5 MB
  • 19. PCA/5. Covariance Matrix vs SVD.mp4 40.6 MB
  • 3. Python For Data Science/5. Python Variables, Booleans and None.mp4 40.1 MB
  • 4. Statistics for Data Science/3. Measure of Variability.mp4 40.1 MB
  • 20. Data Science Career/1. Creating A Data Science Resume.mp4 38.9 MB
  • 19. PCA/11. PCA - Supervised vs Unsupervised.mp4 37.5 MB
  • 16. Ensemble Learning and Random Forests/11. What is Boosting.mp4 37.2 MB
  • 17. Support Vector Machines/1. SVM Outline.mp4 37.0 MB
  • 3. Python For Data Science/6. Getting Started with Google Colab.mp4 36.8 MB
  • 6. NumPy Data Analysis/4. NumPy Array Indexing.mp4 36.4 MB
  • 6. NumPy Data Analysis/1. Intro NumPy Array Data Types.mp4 36.4 MB
  • 4. Statistics for Data Science/4. Measure of Variability Continued.mp4 36.3 MB
  • 15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 35.0 MB
  • 5. Probability & Hypothesis Testing/3. Relative Frequency.mp4 34.3 MB
  • 6. NumPy Data Analysis/2. NumPy Arrays.mp4 33.9 MB
  • 19. PCA/1. PCA Section Overview.mp4 33.3 MB
  • 15. Decision Trees/11. Plot the features importance.mp4 33.2 MB
  • 13. Linear and Logistic Regression/1. Linear Regression Intro.mp4 32.3 MB
  • 20. Data Science Career/6. Personal Branding.mp4 32.0 MB
  • 14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.mp4 32.0 MB
  • 14. K Nearest Neighbors/13. KNN pros and cons.mp4 31.9 MB
  • 20. Data Science Career/4. Getting Started with Freelancing.mp4 31.7 MB
  • 11. Data Cleaning/2. Data Cleaning.mp4 31.7 MB
  • 20. Data Science Career/5. Top Freelance Websites.mp4 31.0 MB
  • 16. Ensemble Learning and Random Forests/4. What is Bagging.mp4 30.9 MB
  • 3. Python For Data Science/16. Python Sets.mp4 30.9 MB
  • 1. Introduction/3. Data Science Job Opportunities.mp4 30.9 MB
  • 14. K Nearest Neighbors/12. KNN use cases.mp4 30.3 MB
  • 16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.mp4 29.0 MB
  • 8. Python Data Visualization/3. Python Data Visualization Implementation.mp4 28.8 MB
  • 5. Probability & Hypothesis Testing/1. What is Exactly is Probability.mp4 28.5 MB
  • 4. Statistics for Data Science/8. Sampling Distribution.mp4 27.7 MB
  • 3. Python For Data Science/8. Python Numbers & Booleans.mp4 26.9 MB
  • 3. Python For Data Science/11. Python For Loops and While Loops.mp4 26.8 MB
  • 16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.mp4 26.8 MB
  • 17. Support Vector Machines/9. SVM with Regression.mp4 26.2 MB
  • 20. Data Science Career/3. How to Contact Recruiters.mp4 25.8 MB
  • 14. K Nearest Neighbors/6. Compare the result with the sklearn library.mp4 25.8 MB
  • 20. Data Science Career/7. Networking Do's and Don'ts.mp4 24.8 MB
  • 4. Statistics for Data Science/5. Measures of Variable Relationship.mp4 24.7 MB
  • 20. Data Science Career/2. Data Science Cover Letter.mp4 24.1 MB
  • 4. Statistics for Data Science/2. Descriptive Statistics.mp4 22.5 MB
  • 3. Python For Data Science/12. Python Lists.mp4 22.5 MB
  • 4. Statistics for Data Science/1. Intro To Statistics.mp4 22.3 MB
  • 17. Support Vector Machines/4. C hyper-parameter.mp4 22.1 MB
  • 16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.mp4 20.6 MB
  • 19. PCA/3. PCA Drawbacks.mp4 20.4 MB
  • 11. Data Cleaning/1. Feature Scaling.mp4 20.3 MB
  • 15. Decision Trees/14. [Optional] Gain Ration.mp4 20.1 MB
  • 12. Feature Selecting and Engineering/1. Feature Engineering.mp4 19.3 MB
  • 3. Python For Data Science/1. What is Programming.mp4 19.2 MB
  • 6. NumPy Data Analysis/6. Broadcasting.mp4 18.7 MB
  • 13. Linear and Logistic Regression/4. Linear Regression Implementation.mp4 18.7 MB
  • 1. Introduction/1. Who is This Course For.mp4 18.0 MB
  • 6. NumPy Data Analysis/5. NumPy Array Computations.mp4 17.8 MB
  • 14. K Nearest Neighbors/8. The decision boundary visualization.mp4 17.8 MB
  • 15. Decision Trees/1. Decision Trees Section Overview.mp4 17.3 MB
  • 3. Python For Data Science/2. Why Python for Data Science.mp4 17.1 MB
  • 16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.mp4 16.9 MB
  • 8. Python Data Visualization/2. Different Data Visualization Libraries in Python.mp4 16.7 MB
  • 13. Linear and Logistic Regression/2. Gradient Descent.mp4 16.7 MB
  • 14. K Nearest Neighbors/2. parametric vs non-parametric models.mp4 16.4 MB
  • 20. Data Science Career/8. Importance of a Website.mp4 16.1 MB
  • 15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 15.8 MB
  • 5. Probability & Hypothesis Testing/2. Expected Values.mp4 15.4 MB
  • 3. Python For Data Science/3. What is Jupyter.mp4 15.3 MB
  • 2. Data Science & Machine Learning Concepts/1. Why We Use Python.mp4 14.2 MB
  • 14. K Nearest Neighbors/1. KNN Overview.mp4 13.5 MB
  • 19. PCA/6. PCA - Main Applications.mp4 10.5 MB
  • 13. Linear and Logistic Regression/5. Logistic Regression.mp4 9.3 MB
  • 3. Python For Data Science/4. What is Google Colab.mp4 8.7 MB
  • 14. K Nearest Neighbors/4. The KNN Intuition.mp4 8.5 MB
  • 4. Statistics for Data Science/7. Measure of Asymmetry.mp4 7.1 MB
  • 9. Machine Learning/1.1 Supervised Learning.pdf 856.8 kB
  • 18. K-means/1.1 Unsupervised Learning.pdf 651.8 kB
  • 3. Python For Data Science/3.1 Jupyter Notebook.pdf 314.5 kB
  • 3. Python For Data Science/2.2 Python Basics.pdf 130.8 kB
  • 7. Pandas Data Analysis/1.1 Pandas.pdf 112.8 kB
  • 6. NumPy Data Analysis/1.1 NumPy Basics.pdf 79.0 kB
  • 7. Pandas Data Analysis/1.2 Pandas Basics.pdf 78.9 kB
  • 3. Python For Data Science/2.1 Importing Python Data.pdf 63.0 kB
  • 19. PCA/7. PCA - Image Compression.srt 40.3 kB
  • 13. Linear and Logistic Regression/3. Linear Regression + Correlation Methods.srt 39.5 kB
  • 9. Machine Learning/1. Introduction To Machine Learning.srt 37.9 kB
  • 8. Python Data Visualization/1. Data Visualization Overview.srt 37.7 kB
  • 14. K Nearest Neighbors/3. EDA on Iris Dataset.srt 32.4 kB
  • 3. Python For Data Science/7. Python Operators.srt 32.2 kB
  • 15. Decision Trees/7. ID3 - Putting Everything Together.srt 31.8 kB
  • 1. Introduction/6. How To Get a Data Science Job.srt 31.4 kB
  • 16. Ensemble Learning and Random Forests/6. Implementing Random Forests from scratch Part 1.srt 30.8 kB
  • 18. K-means/1. Unsupervised Machine Learning Intro.srt 30.1 kB
  • 15. Decision Trees/3. What is Entropy and Information Gain.srt 30.0 kB
  • 18. K-means/2. Unsupervised Machine Learning Continued.srt 29.9 kB
  • 18. K-means/3. Representing Clusters.srt 29.0 kB
  • 3. Python For Data Science/15. Python Dictionaries.srt 28.4 kB
  • 1. Introduction/5. What is a Data Scientist.srt 27.5 kB
  • 7. Pandas Data Analysis/2. Introduction to Pandas Continued.srt 27.5 kB
  • 17. Support Vector Machines/6. SVM - Kernel Types.srt 27.4 kB
  • 19. PCA/9. PCA - Biplot and the Screen Plot.srt 27.0 kB
  • 3. Python For Data Science/19. Object Oriented Programming in Python.srt 26.3 kB
  • 15. Decision Trees/8. Evaluating our ID3 implementation.srt 25.1 kB
  • 15. Decision Trees/13. Pruning.srt 24.9 kB
  • 15. Decision Trees/2. EDA on Adult Dataset.srt 24.3 kB
  • 2. Data Science & Machine Learning Concepts/4. Machine Learning Concepts & Algorithms.srt 24.1 kB
  • 2. Data Science & Machine Learning Concepts/3. What is Machine Learning.srt 23.5 kB
  • 7. Pandas Data Analysis/1. Introduction to Pandas.srt 22.8 kB
  • 4. Statistics for Data Science/6. Inferential Statistics.srt 22.6 kB
  • 2. Data Science & Machine Learning Concepts/2. What is Data Science.srt 21.7 kB
  • 19. PCA/8. PCA Data Preprocessing.srt 21.6 kB
  • 16. Ensemble Learning and Random Forests/13. AdaBoost Part 2.srt 21.4 kB
  • 3. Python For Data Science/18. Python Functions.srt 21.3 kB
  • 17. Support Vector Machines/7. SVM with Linear Dataset (Iris).srt 20.3 kB
  • 3. Python For Data Science/13. More about Lists.srt 19.9 kB
  • 1. Introduction/7. Data Science Projects Overview.srt 19.5 kB
  • 10. Data Loading & Exploration/1. Exploratory Data Analysis.srt 19.5 kB
  • 17. Support Vector Machines/3. Hard vs Soft Margins.srt 19.4 kB
  • 19. PCA/4. PCA Algorithm Steps (Mathematics).srt 19.0 kB
  • 17. Support Vector Machines/8. SVM with Non-linear Dataset.srt 18.8 kB
  • 6. NumPy Data Analysis/1. Intro NumPy Array Data Types.srt 18.7 kB
  • 4. Statistics for Data Science/3. Measure of Variability.srt 18.6 kB
  • 17. Support Vector Machines/5. Kernel Trick.srt 18.6 kB
  • 3. Python For Data Science/10. Python Conditional Statements.srt 18.6 kB
  • 3. Python For Data Science/17. Compound Data Types & When to use each one.srt 18.4 kB
  • 2. Data Science & Machine Learning Concepts/6. Machine Learning vs Deep Learning.srt 18.3 kB
  • 16. Ensemble Learning and Random Forests/2. What is Ensemble Learning.srt 17.8 kB
  • 14. K Nearest Neighbors/5. Implement the KNN algorithm from scratch.srt 17.6 kB
  • 6. NumPy Data Analysis/3. NumPy Arrays Basics.srt 17.2 kB
  • 3. Python For Data Science/9. Python Strings.srt 16.5 kB
  • 15. Decision Trees/12. Decision Trees Hyper-parameters.srt 16.5 kB
  • 17. Support Vector Machines/2. SVM intuition.srt 16.4 kB
  • 2. Data Science & Machine Learning Concepts/5. What is Deep Learning.srt 16.2 kB
  • 1. Introduction/4. Data Science Job Roles.srt 16.1 kB
  • 3. Python For Data Science/5. Python Variables, Booleans and None.srt 15.6 kB
  • 15. Decision Trees/10. Visualizing the tree.srt 15.4 kB
  • 3. Python For Data Science/14. Python Tuples.srt 15.3 kB
  • 15. Decision Trees/4. The Decision Tree ID3 algorithm from scratch Part 1.srt 15.3 kB
  • 14. K Nearest Neighbors/7. Hyperparameter tuning using the cross-validation.srt 15.0 kB
  • 19. PCA/2. What is PCA.srt 14.9 kB
  • 5. Probability & Hypothesis Testing/4. Hypothesis Testing Overview.srt 14.9 kB
  • 19. PCA/10. PCA - Feature Scaling and Screen Plot.srt 14.7 kB
  • 6. NumPy Data Analysis/4. NumPy Array Indexing.srt 14.4 kB
  • 3. Python For Data Science/16. Python Sets.srt 13.8 kB
  • 4. Statistics for Data Science/4. Measure of Variability Continued.srt 13.6 kB
  • 8. Python Data Visualization/3. Python Data Visualization Implementation.srt 12.7 kB
  • 3. Python For Data Science/6. Getting Started with Google Colab.srt 12.7 kB
  • 15. Decision Trees/9. Compare with Sklearn implementation.srt 12.6 kB
  • 13. Linear and Logistic Regression/1. Linear Regression Intro.srt 12.5 kB
  • 11. Data Cleaning/1. Feature Scaling.srt 11.9 kB
  • 11. Data Cleaning/2. Data Cleaning.srt 11.8 kB
  • 4. Statistics for Data Science/1. Intro To Statistics.srt 11.4 kB
  • 16. Ensemble Learning and Random Forests/3. What is Bootstrap Sampling.srt 11.4 kB
  • 6. NumPy Data Analysis/2. NumPy Arrays.srt 11.4 kB
  • 19. PCA/12. PCA - Visualization.srt 11.2 kB
  • 3. Python For Data Science/11. Python For Loops and While Loops.srt 11.0 kB
  • 4. Statistics for Data Science/5. Measures of Variable Relationship.srt 11.0 kB
  • 15. Decision Trees/5. The Decision Tree ID3 algorithm from scratch Part 2.srt 10.9 kB
  • 15. Decision Trees/15. Decision Trees Pros and Cons.srt 10.9 kB
  • 20. Data Science Career/1. Creating A Data Science Resume.srt 10.8 kB
  • 1. Introduction/2. Data Science + Machine Learning Marketplace.srt 10.7 kB
  • 4. Statistics for Data Science/8. Sampling Distribution.srt 10.5 kB
  • 4. Statistics for Data Science/2. Descriptive Statistics.srt 10.3 kB
  • 16. Ensemble Learning and Random Forests/5. Out-of-Bag Error (OOB Error).srt 10.2 kB
  • 3. Python For Data Science/8. Python Numbers & Booleans.srt 9.8 kB
  • 14. K Nearest Neighbors/11. Curse of dimensionality.srt 9.8 kB
  • 12. Feature Selecting and Engineering/1. Feature Engineering.srt 9.7 kB
  • 3. Python For Data Science/1. What is Programming.srt 9.2 kB
  • 8. Python Data Visualization/2. Different Data Visualization Libraries in Python.srt 9.0 kB
  • 5. Probability & Hypothesis Testing/3. Relative Frequency.srt 8.9 kB
  • 6. NumPy Data Analysis/5. NumPy Array Computations.srt 8.8 kB
  • 13. Linear and Logistic Regression/2. Gradient Descent.srt 8.6 kB
  • 20. Data Science Career/5. Top Freelance Websites.srt 8.6 kB
  • 16. Ensemble Learning and Random Forests/7. Implementing Random Forests from scratch Part 2.srt 8.5 kB
  • 14. K Nearest Neighbors/10. Feature scaling in KNN.srt 8.3 kB
  • 17. Support Vector Machines/9. SVM with Regression.srt 8.2 kB
  • 16. Ensemble Learning and Random Forests/10. Random Forests Pros and Cons.srt 8.0 kB
  • 14. K Nearest Neighbors/13. KNN pros and cons.srt 7.9 kB
  • 14. K Nearest Neighbors/9. Manhattan vs Euclidean Distance.srt 7.9 kB
  • 15. Decision Trees/11. Plot the features importance.srt 7.9 kB
  • 16. Ensemble Learning and Random Forests/4. What is Bagging.srt 7.9 kB
  • 17. Support Vector Machines/1. SVM Outline.srt 7.6 kB
  • 20. Data Science Career/3. How to Contact Recruiters.srt 7.5 kB
  • 19. PCA/11. PCA - Supervised vs Unsupervised.srt 7.3 kB
  • 3. Python For Data Science/12. Python Lists.srt 7.3 kB
  • 20. Data Science Career/4. Getting Started with Freelancing.srt 7.2 kB
  • 14. K Nearest Neighbors/8. The decision boundary visualization.srt 7.2 kB
  • 19. PCA/1. PCA Section Overview.srt 7.2 kB
  • 13. Linear and Logistic Regression/4. Linear Regression Implementation.srt 7.0 kB
  • 1. Introduction/3. Data Science Job Opportunities.srt 7.0 kB
  • 16. Ensemble Learning and Random Forests/11. What is Boosting.srt 7.0 kB
  • 3. Python For Data Science/2. Why Python for Data Science.srt 6.9 kB
  • 5. Probability & Hypothesis Testing/1. What is Exactly is Probability.srt 6.9 kB
  • 19. PCA/5. Covariance Matrix vs SVD.srt 6.7 kB
  • 20. Data Science Career/6. Personal Branding.srt 6.6 kB
  • 6. NumPy Data Analysis/6. Broadcasting.srt 6.5 kB
  • 20. Data Science Career/7. Networking Do's and Don'ts.srt 6.4 kB
  • 17. Support Vector Machines/10. SMV - Project Overview.srt 6.4 kB
  • 20. Data Science Career/2. Data Science Cover Letter.srt 6.1 kB
  • 16. Ensemble Learning and Random Forests/9. Random Forests Hyper-Parameters.srt 6.1 kB
  • 3. Python For Data Science/3. What is Jupyter.srt 6.1 kB
  • 15. Decision Trees/6. The Decision Tree ID3 algorithm from scratch Part 3.srt 5.9 kB
  • 17. Support Vector Machines/4. C hyper-parameter.srt 5.8 kB
  • 15. Decision Trees/1. Decision Trees Section Overview.srt 5.7 kB
  • 16. Ensemble Learning and Random Forests/12. AdaBoost Part 1.srt 5.6 kB
  • 16. Ensemble Learning and Random Forests/1. Ensemble Learning Section Overview.srt 5.3 kB
  • 14. K Nearest Neighbors/6. Compare the result with the sklearn library.srt 5.2 kB
  • 16. Ensemble Learning and Random Forests/8. Compare with sklearn implementation.srt 5.1 kB
  • 13. Linear and Logistic Regression/5. Logistic Regression.srt 5.0 kB
  • 2. Data Science & Machine Learning Concepts/1. Why We Use Python.srt 5.0 kB
  • 19. PCA/3. PCA Drawbacks.srt 5.0 kB
  • 3. Python For Data Science/4. What is Google Colab.srt 5.0 kB
  • 14. K Nearest Neighbors/12. KNN use cases.srt 5.0 kB
  • 20. Data Science Career/8. Importance of a Website.srt 4.9 kB
  • 14. K Nearest Neighbors/2. parametric vs non-parametric models.srt 4.8 kB
  • 14. K Nearest Neighbors/1. KNN Overview.srt 4.4 kB
  • 5. Probability & Hypothesis Testing/2. Expected Values.srt 4.2 kB
  • 1. Introduction/1. Who is This Course For.srt 4.0 kB
  • 19. PCA/6. PCA - Main Applications.srt 4.0 kB
  • 15. Decision Trees/14. [Optional] Gain Ration.srt 3.8 kB
  • 15. Decision Trees/16. [Project] Predict whether income exceeds $50Kyr - Overview.srt 3.7 kB
  • 14. K Nearest Neighbors/4. The KNN Intuition.srt 3.1 kB
  • 4. Statistics for Data Science/7. Measure of Asymmetry.srt 2.8 kB
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  • 16. Ensemble Learning and Random Forests/[CourseClub.Me].url 122 Bytes
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  • [CourseClub.Me].url 122 Bytes
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