[DesireCourse.Net] Udemy - Machine Learning, incl. Deep Learning, with R

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[DesireCourse.Net] Udemy - Machine Learning, incl. Deep Learning, with R

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收录时间：2021-04-25
最近下载：2026-04-15
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文件列表

  • 28. Convolutional Neural Networks/4. Convolutional Neural Networks Lab (Coding).mp4 199.0 MB
  • 6. Regularization/2. Regularization Lab.mp4 198.8 MB
  • 18. Hierarchical Clustering/3. Hierarchical Clustering Lab.mp4 198.4 MB
  • 5. Model Preparation and Evaluation/6. Resampling Techniques Lab.mp4 197.8 MB
  • 17. kmeans/2. kmeans Lab.mp4 167.6 MB
  • 31. Recurrent Neural Networks/3. LSTM Univariate, Multistep Timeseries Prediction (Coding).mp4 153.7 MB
  • 31. Recurrent Neural Networks/5. LSTM Multivariate, Multistep Timeseries Prediction (Coding).mp4 148.4 MB
  • 24. ----- Reinforcement Learning -----/6. Upper Confidence Bound Lab (Coding 12).mp4 145.0 MB
  • 4. Regression/10. Multivariate Regression Lab.mp4 142.3 MB
  • 8. Classification Basics/7. ROC Curve Lab 33 (ROC, AUC, Cost Function).mp4 142.1 MB
  • 27. Deep Learning Classification/6. Multi-Label Classification Lab (Coding 23).mp4 135.1 MB
  • 21. Principal Component Analysis (PCA)/2. PCA Lab.mp4 133.2 MB
  • 1. Introduction/6. Teaser Lab.mp4 132.7 MB
  • 4. Regression/12. Multivariate Regression Solution.mp4 128.6 MB
  • 27. Deep Learning Classification/2. Binary Classification Lab (Coding 12).mp4 126.9 MB
  • 9. Decision Trees/3. Decision Trees Lab (Coding).mp4 126.9 MB
  • 26. Deep Learning Regression/2. Multi-Target Regression Lab (Coding 12).mp4 124.8 MB
  • 8. Classification Basics/5. ROC Curve Lab 13 (Data Prep, Modeling).mp4 123.8 MB
  • 4. Regression/8. Polynomial Regression Lab.mp4 123.3 MB
  • 5. Model Preparation and Evaluation/4. Train Validation Test Split Lab.mp4 123.1 MB
  • 15. Apriori/4. Apriori Lab (Coding 22).mp4 119.0 MB
  • 19. Dbscan/2. Dbscan Lab.mp4 116.8 MB
  • 10. Random Forests/4. Random Forest Lab (Coding 12).mp4 115.2 MB
  • 27. Deep Learning Classification/5. Multi-Label Classification Lab (Coding 13).mp4 115.1 MB
  • 10. Random Forests/5. Random Forest Lab (Coding 22).mp4 112.3 MB
  • 17. kmeans/4. kmeans Solution.mp4 111.5 MB
  • 29. Autoencoders/3. Autoencoders Lab (Coding).mp4 110.6 MB
  • 2. R Refresher/5. Data Manipulation Lab.mp4 109.7 MB
  • 2. R Refresher/7. Data Reshaping Lab.mp4 108.1 MB
  • 2. R Refresher/1. R and RStudio Installation.mp4 107.3 MB
  • 15. Apriori/6. Apriori Solution.mp4 105.0 MB
  • 30. Transfer Learning and Pretrained Models/3. Transfer Learning and Pretrained Models Lab (Coding).mp4 104.2 MB
  • 26. Deep Learning Regression/3. Multi-Target Regression Lab (Coding 22).mp4 103.6 MB
  • 11. Logistic Regression/3. Logistic Regression Lab (Coding 12).mp4 96.4 MB
  • 23. Factor Analysis/4. Factor Analysis Lab (Coding 22).mp4 96.2 MB
  • 4. Regression/4. Univariate Regression Lab.mp4 92.7 MB
  • 21. Principal Component Analysis (PCA)/4. PCA Solution.mp4 84.9 MB
  • 12. Support Vector Machines/3. Support Vector Machines Lab (Coding 12).mp4 82.6 MB
  • 23. Factor Analysis/3. Factor Analysis Lab (Coding 12).mp4 82.5 MB
  • 15. Apriori/3. Apriori Lab (Coding 12).mp4 76.9 MB
  • 25. ----- Deep Learning -----/11. Python and Keras Installation.mp4 76.2 MB
  • 4. Regression/6. Univariate Regression Solution.mp4 74.8 MB
  • 8. Classification Basics/6. ROC Curve Lab 23 (Confusion Matrix and ROC).mp4 74.2 MB
  • 22. t-SNE/3. t-SNE Lab (Mnist).mp4 73.8 MB
  • 27. Deep Learning Classification/3. Binary Classification Lab (Coding 22).mp4 71.2 MB
  • 24. ----- Reinforcement Learning -----/7. Upper Confidence Bound Lab (Coding 22).mp4 70.4 MB
  • 2. R Refresher/3. Rmarkdown Lab.mp4 69.0 MB
  • 11. Logistic Regression/4. Logistic Regression Lab (Coding 22).mp4 66.2 MB
  • 27. Deep Learning Classification/7. Multi-Label Classification Lab (Coding 33).mp4 65.8 MB
  • 28. Convolutional Neural Networks/6. Semantic Segmentation 101.mp4 60.8 MB
  • 22. t-SNE/2. t-SNE Lab (Sphere).mp4 60.2 MB
  • 5. Model Preparation and Evaluation/1. Underfitting Overfitting 101.mp4 58.8 MB
  • 24. ----- Reinforcement Learning -----/2. Upper Confidence Bound 101.mp4 52.9 MB
  • 8. Classification Basics/2. ROC Curve 101.mp4 50.3 MB
  • 24. ----- Reinforcement Learning -----/3. Upper Confidence Bound Interactive.mp4 48.8 MB
  • 28. Convolutional Neural Networks/1. Convolutional Neural Networks 101.mp4 46.4 MB
  • 8. Classification Basics/3. ROC Curve Interactive.mp4 45.6 MB
  • 12. Support Vector Machines/4. Support Vector Machines Lab (Coding 22).mp4 44.2 MB
  • 21. Principal Component Analysis (PCA)/1. PCA 101.mp4 43.8 MB
  • 24. ----- Reinforcement Learning -----/1. Reinforcement Learning 101.mp4 39.3 MB
  • 5. Model Preparation and Evaluation/3. Train Validation Test Split Interactive.mp4 37.8 MB
  • 23. Factor Analysis/1. Factor Analysis 101.mp4 36.7 MB
  • 18. Hierarchical Clustering/2. Hierarchical Clustering Interactive.mp4 35.8 MB
  • 30. Transfer Learning and Pretrained Models/1. Transfer Learning and Pretrained Models 101.mp4 34.4 MB
  • 18. Hierarchical Clustering/1. Hierarchical Clustering 101.mp4 34.0 MB
  • 17. kmeans/1. kmeans 101.mp4 33.3 MB
  • 19. Dbscan/1. Dbscan 101.mp4 32.8 MB
  • 1. Introduction/3. Machine Learning 101.mp4 32.7 MB
  • 15. Apriori/1. Apriori 101.mp4 31.3 MB
  • 1. Introduction/2. AI 101.mp4 31.0 MB
  • 31. Recurrent Neural Networks/1. Recurrent Neural Networks 101.mp4 30.9 MB
  • 8. Classification Basics/1. Confusion Matrix 101.mp4 30.3 MB
  • 1. Introduction/4. Models.mp4 29.0 MB
  • 11. Logistic Regression/1. Logistic Regression 101.mp4 29.0 MB
  • 17. kmeans/3. kmeans Exercise.mp4 28.9 MB
  • 25. ----- Deep Learning -----/1. Deep Learning General Overview.mp4 27.7 MB
  • 4. Regression/2. Univariate Regression 101.mp4 26.8 MB
  • 28. Convolutional Neural Networks/7. Semantic Segmentation Lab (Intro).mp4 26.7 MB
  • 28. Convolutional Neural Networks/8. Semantic Segmentation Lab (Coding).mp4 26.7 MB
  • 27. Deep Learning Classification/4. Multi-Label Classification Lab (Intro).mp4 25.6 MB
  • 6. Regularization/1. Regularization 101.mp4 24.9 MB
  • 28. Convolutional Neural Networks/5. Convolutional Neural Networks Exercise.mp4 24.9 MB
  • 4. Regression/9. Multivariate Regression 101.mp4 23.5 MB
  • 25. ----- Deep Learning -----/8. Optimizer.mp4 23.4 MB
  • 10. Random Forests/6. Random Forest Exercise.mp4 23.1 MB
  • 4. Regression/3. Univariate Regression Interactive.mp4 22.9 MB
  • 12. Support Vector Machines/1. Support Vector Machines 101.mp4 22.9 MB
  • 25. ----- Deep Learning -----/5. Layer Types.mp4 22.8 MB
  • 12. Support Vector Machines/5. Support Vector Machines Exercise.mp4 22.2 MB
  • 14. ----- Association Rules -----/1. Association Rules 101.mp4 21.9 MB
  • 25. ----- Deep Learning -----/6. Activation Functions.mp4 21.7 MB
  • 9. Decision Trees/1. Decision Trees 101.mp4 21.5 MB
  • 22. t-SNE/1. t-SNE 101.mp4 20.9 MB
  • 25. ----- Deep Learning -----/4. From Perceptron to Neural Networks.mp4 20.7 MB
  • 2. R Refresher/6. Data Reshaping 101.mp4 19.7 MB
  • 4. Regression/5. Univariate Regression Exercise.mp4 19.0 MB
  • 28. Convolutional Neural Networks/2. Convolutional Neural Networks Interactive.mp4 19.0 MB
  • 15. Apriori/2. Apriori Lab (Intro).mp4 19.0 MB
  • 4. Regression/1. Regression Types 101.mp4 18.6 MB
  • 10. Random Forests/2. Random Forests Interactive.mp4 18.4 MB
  • 15. Apriori/5. Apriori Exercise.mp4 18.1 MB
  • 5. Model Preparation and Evaluation/5. Resampling Techniques 101.mp4 18.0 MB
  • 29. Autoencoders/1. Autoencoders 101.mp4 17.5 MB
  • 23. Factor Analysis/2. Factor Analysis Lab (Intro).mp4 17.3 MB
  • 27. Deep Learning Classification/1. Binary Classification Lab (Intro).mp4 16.0 MB
  • 21. Principal Component Analysis (PCA)/3. PCA Exercise.mp4 16.0 MB
  • 29. Autoencoders/2. Autoencoders Lab (Intro).mp4 15.8 MB
  • 10. Random Forests/3. Random Forest Lab (Intro).mp4 15.6 MB
  • 9. Decision Trees/4. Decision Trees Exercise.mp4 14.8 MB
  • 25. ----- Deep Learning -----/7. Loss Function.mp4 14.6 MB
  • 4. Regression/11. Multivariate Regression Exercise.mp4 14.4 MB
  • 30. Transfer Learning and Pretrained Models/2. Transfer Learning and Pretrained Models Lab (Introduction).mp4 14.3 MB
  • 31. Recurrent Neural Networks/2. LSTM Univariate, Multistep Timeseries Prediction (Intro).mp4 14.3 MB
  • 12. Support Vector Machines/2. Support Vector Machines Lab (Intro).mp4 14.3 MB
  • 5. Model Preparation and Evaluation/2. Train Validation Test Split 101.mp4 14.2 MB
  • 26. Deep Learning Regression/1. Multi-Target Regression Lab (Intro).mp4 14.0 MB
  • 23. Factor Analysis/5. Factor Analysis Exercise.mp4 13.9 MB
  • 24. ----- Reinforcement Learning -----/5. Upper Confidence Bound Lab (Intro).mp4 13.8 MB
  • 8. Classification Basics/4. ROC Curve Lab Intro.mp4 13.2 MB
  • 25. ----- Deep Learning -----/2. Deep Learning Modeling 101.mp4 13.0 MB
  • 2. R Refresher/8. Packages Preparation Lab.mp4 12.9 MB
  • 28. Convolutional Neural Networks/3. Convolutional Neural Networks Lab (Intro).mp4 12.7 MB
  • 13. Ensemble Models/1. Ensemble Models 101.mp4 12.6 MB
  • 31. Recurrent Neural Networks/4. LSTM Multivariate, Multistep Timeseries Prediction (Intro).mp4 12.6 MB
  • 4. Regression/7. Polynomial Regression 101.mp4 11.9 MB
  • 25. ----- Deep Learning -----/3. Performance.mp4 11.7 MB
  • 10. Random Forests/1. Random Forests 101.mp4 11.3 MB
  • 11. Logistic Regression/5. Logistic Regression Exercise.mp4 11.2 MB
  • 9. Decision Trees/2. Decision Trees Lab (Intro).mp4 11.1 MB
  • 1. Introduction/1. Course Overview.mp4 10.9 MB
  • 16. ----- Clustering -----/1. Clustering Overview.mp4 10.6 MB
  • 25. ----- Deep Learning -----/9. Deep Learning Frameworks.mp4 9.9 MB
  • 2. R Refresher/4. Piping 101.mp4 9.9 MB
  • 7. ----- Classification -----/2. How to get the code.mp4 9.3 MB
  • 2. R Refresher/2. How to get the code.mp4 9.3 MB
  • 24. ----- Reinforcement Learning -----/4. How to get the code.mp4 9.3 MB
  • 14. ----- Association Rules -----/2. How to get the code.mp4 9.3 MB
  • 3. ----- Regression, Model Preparation, and Regularization -----/2. How to get the code.mp4 9.3 MB
  • 25. ----- Deep Learning -----/10. How to get the code.mp4 9.3 MB
  • 16. ----- Clustering -----/2. How to get the code.mp4 9.2 MB
  • 11. Logistic Regression/2. Logistic Regression Lab (Intro).mp4 9.2 MB
  • 1. Introduction/5. Teaser Overview.mp4 6.5 MB
  • 1. Introduction/6.2 PCA_Teaser_Final.html.html 5.1 MB
  • 28. Convolutional Neural Networks/4. Convolutional Neural Networks Lab (Coding).vtt 16.1 kB
  • 5. Model Preparation and Evaluation/6. Resampling Techniques Lab.vtt 15.0 kB
  • 18. Hierarchical Clustering/3. Hierarchical Clustering Lab.vtt 14.8 kB
  • 6. Regularization/2. Regularization Lab.vtt 14.1 kB
  • 24. ----- Reinforcement Learning -----/2. Upper Confidence Bound 101.vtt 13.5 kB
  • 19. Dbscan/2. Dbscan Lab.vtt 13.0 kB
  • 9. Decision Trees/3. Decision Trees Lab (Coding).vtt 13.0 kB
  • 17. kmeans/2. kmeans Lab.vtt 12.7 kB
  • 1. Introduction/6. Teaser Lab.vtt 12.5 kB
  • 5. Model Preparation and Evaluation/1. Underfitting Overfitting 101.vtt 12.5 kB
  • 21. Principal Component Analysis (PCA)/2. PCA Lab.vtt 12.3 kB
  • 31. Recurrent Neural Networks/3. LSTM Univariate, Multistep Timeseries Prediction (Coding).vtt 12.2 kB
  • 4. Regression/10. Multivariate Regression Lab.vtt 12.2 kB
  • 4. Regression/8. Polynomial Regression Lab.vtt 11.5 kB
  • 24. ----- Reinforcement Learning -----/6. Upper Confidence Bound Lab (Coding 12).vtt 11.2 kB
  • 31. Recurrent Neural Networks/5. LSTM Multivariate, Multistep Timeseries Prediction (Coding).vtt 11.1 kB
  • 10. Random Forests/4. Random Forest Lab (Coding 12).vtt 10.7 kB
  • 28. Convolutional Neural Networks/1. Convolutional Neural Networks 101.vtt 10.6 kB
  • 2. R Refresher/7. Data Reshaping Lab.vtt 10.5 kB
  • 5. Model Preparation and Evaluation/4. Train Validation Test Split Lab.vtt 10.3 kB
  • 4. Regression/4. Univariate Regression Lab.vtt 10.3 kB
  • 4. Regression/12. Multivariate Regression Solution.vtt 10.3 kB
  • 8. Classification Basics/7. ROC Curve Lab 33 (ROC, AUC, Cost Function).vtt 10.2 kB
  • 27. Deep Learning Classification/6. Multi-Label Classification Lab (Coding 23).vtt 10.0 kB
  • 8. Classification Basics/5. ROC Curve Lab 13 (Data Prep, Modeling).vtt 10.0 kB
  • 27. Deep Learning Classification/2. Binary Classification Lab (Coding 12).vtt 9.6 kB
  • 2. R Refresher/5. Data Manipulation Lab.vtt 9.3 kB
  • 26. Deep Learning Regression/2. Multi-Target Regression Lab (Coding 12).vtt 9.3 kB
  • 15. Apriori/6. Apriori Solution.vtt 9.2 kB
  • 29. Autoencoders/3. Autoencoders Lab (Coding).vtt 9.1 kB
  • 21. Principal Component Analysis (PCA)/1. PCA 101.vtt 9.0 kB
  • 23. Factor Analysis/1. Factor Analysis 101.vtt 9.0 kB
  • 27. Deep Learning Classification/5. Multi-Label Classification Lab (Coding 13).vtt 8.8 kB
  • 2. R Refresher/1. R and RStudio Installation.vtt 8.7 kB
  • 30. Transfer Learning and Pretrained Models/3. Transfer Learning and Pretrained Models Lab (Coding).vtt 8.6 kB
  • 10. Random Forests/5. Random Forest Lab (Coding 22).vtt 8.4 kB
  • 2. R Refresher/3. Rmarkdown Lab.vtt 8.4 kB
  • 24. ----- Reinforcement Learning -----/1. Reinforcement Learning 101.vtt 8.3 kB
  • 18. Hierarchical Clustering/1. Hierarchical Clustering 101.vtt 8.3 kB
  • 28. Convolutional Neural Networks/6. Semantic Segmentation 101.vtt 8.0 kB
  • 1. Introduction/3. Machine Learning 101.vtt 7.9 kB
  • 17. kmeans/1. kmeans 101.vtt 7.9 kB
  • 26. Deep Learning Regression/3. Multi-Target Regression Lab (Coding 22).vtt 7.8 kB
  • 31. Recurrent Neural Networks/1. Recurrent Neural Networks 101.vtt 7.8 kB
  • 15. Apriori/4. Apriori Lab (Coding 22).vtt 7.6 kB
  • 15. Apriori/1. Apriori 101.vtt 7.6 kB
  • 11. Logistic Regression/1. Logistic Regression 101.vtt 7.6 kB
  • 11. Logistic Regression/3. Logistic Regression Lab (Coding 12).vtt 7.5 kB
  • 5. Model Preparation and Evaluation/3. Train Validation Test Split Interactive.vtt 7.5 kB
  • 8. Classification Basics/2. ROC Curve 101.vtt 7.3 kB
  • 23. Factor Analysis/4. Factor Analysis Lab (Coding 22).vtt 7.1 kB
  • 24. ----- Reinforcement Learning -----/3. Upper Confidence Bound Interactive.vtt 7.0 kB
  • 25. ----- Deep Learning -----/8. Optimizer.vtt 7.0 kB
  • 12. Support Vector Machines/3. Support Vector Machines Lab (Coding 12).vtt 7.0 kB
  • 21. Principal Component Analysis (PCA)/4. PCA Solution.vtt 6.9 kB
  • 23. Factor Analysis/3. Factor Analysis Lab (Coding 12).vtt 6.7 kB
  • 22. t-SNE/1. t-SNE 101.vtt 6.6 kB
  • 8. Classification Basics/1. Confusion Matrix 101.vtt 6.5 kB
  • 4. Regression/6. Univariate Regression Solution.vtt 6.4 kB
  • 25. ----- Deep Learning -----/11. Python and Keras Installation.vtt 6.4 kB
  • 8. Classification Basics/3. ROC Curve Interactive.vtt 6.3 kB
  • 4. Regression/2. Univariate Regression 101.vtt 6.3 kB
  • 6. Regularization/1. Regularization 101.vtt 6.3 kB
  • 15. Apriori/3. Apriori Lab (Coding 12).vtt 6.2 kB
  • 9. Decision Trees/1. Decision Trees 101.vtt 6.1 kB
  • 1. Introduction/4. Models.vtt 6.0 kB
  • 22. t-SNE/3. t-SNE Lab (Mnist).vtt 5.9 kB
  • 11. Logistic Regression/4. Logistic Regression Lab (Coding 22).vtt 5.9 kB
  • 18. Hierarchical Clustering/2. Hierarchical Clustering Interactive.vtt 5.9 kB
  • 12. Support Vector Machines/1. Support Vector Machines 101.vtt 5.7 kB
  • 1. Introduction/2. AI 101.vtt 5.7 kB
  • 14. ----- Association Rules -----/1. Association Rules 101.vtt 5.6 kB
  • 30. Transfer Learning and Pretrained Models/1. Transfer Learning and Pretrained Models 101.vtt 5.5 kB
  • 27. Deep Learning Classification/3. Binary Classification Lab (Coding 22).vtt 5.4 kB
  • 22. t-SNE/2. t-SNE Lab (Sphere).vtt 5.3 kB
  • 27. Deep Learning Classification/7. Multi-Label Classification Lab (Coding 33).vtt 5.3 kB
  • 8. Classification Basics/6. ROC Curve Lab 23 (Confusion Matrix and ROC).vtt 5.3 kB
  • 5. Model Preparation and Evaluation/5. Resampling Techniques 101.vtt 5.2 kB
  • 24. ----- Reinforcement Learning -----/7. Upper Confidence Bound Lab (Coding 22).vtt 5.1 kB
  • 19. Dbscan/1. Dbscan 101.vtt 5.1 kB
  • 4. Regression/9. Multivariate Regression 101.vtt 5.0 kB
  • 25. ----- Deep Learning -----/2. Deep Learning Modeling 101.vtt 4.8 kB
  • 25. ----- Deep Learning -----/6. Activation Functions.vtt 4.7 kB
  • 25. ----- Deep Learning -----/5. Layer Types.vtt 4.7 kB
  • 25. ----- Deep Learning -----/1. Deep Learning General Overview.vtt 4.4 kB
  • 4. Regression/1. Regression Types 101.vtt 4.4 kB
  • 12. Support Vector Machines/4. Support Vector Machines Lab (Coding 22).vtt 4.2 kB
  • 4. Regression/3. Univariate Regression Interactive.vtt 4.2 kB
  • 25. ----- Deep Learning -----/4. From Perceptron to Neural Networks.vtt 4.2 kB
  • 25. ----- Deep Learning -----/7. Loss Function.vtt 3.9 kB
  • 13. Ensemble Models/1. Ensemble Models 101.vtt 3.8 kB
  • 2. R Refresher/6. Data Reshaping 101.vtt 3.6 kB
  • 28. Convolutional Neural Networks/2. Convolutional Neural Networks Interactive.vtt 3.5 kB
  • 10. Random Forests/2. Random Forests Interactive.vtt 3.5 kB
  • 1. Introduction/6.1 PCA_Teaser.zip.zip 3.4 kB
  • 1. Introduction/1. Course Overview.vtt 3.2 kB
  • 17. kmeans/3. kmeans Exercise.vtt 3.2 kB
  • 27. Deep Learning Classification/4. Multi-Label Classification Lab (Intro).vtt 3.1 kB
  • 5. Model Preparation and Evaluation/2. Train Validation Test Split 101.vtt 3.1 kB
  • 10. Random Forests/1. Random Forests 101.vtt 3.0 kB
  • 16. ----- Clustering -----/1. Clustering Overview.vtt 3.0 kB
  • 25. ----- Deep Learning -----/3. Performance.vtt 3.0 kB
  • 2. R Refresher/4. Piping 101.vtt 2.9 kB
  • 29. Autoencoders/1. Autoencoders 101.vtt 2.8 kB
  • 28. Convolutional Neural Networks/7. Semantic Segmentation Lab (Intro).vtt 2.8 kB
  • 28. Convolutional Neural Networks/8. Semantic Segmentation Lab (Coding).vtt 2.8 kB
  • 25. ----- Deep Learning -----/9. Deep Learning Frameworks.vtt 2.7 kB
  • 28. Convolutional Neural Networks/5. Convolutional Neural Networks Exercise.vtt 2.6 kB
  • 4. Regression/7. Polynomial Regression 101.vtt 2.5 kB
  • 10. Random Forests/6. Random Forest Exercise.vtt 2.4 kB
  • 4. Regression/5. Univariate Regression Exercise.vtt 2.3 kB
  • 15. Apriori/5. Apriori Exercise.vtt 2.2 kB
  • 12. Support Vector Machines/5. Support Vector Machines Exercise.vtt 2.1 kB
  • 4. Regression/11. Multivariate Regression Exercise.vtt 2.0 kB
  • 21. Principal Component Analysis (PCA)/3. PCA Exercise.vtt 1.9 kB
  • 30. Transfer Learning and Pretrained Models/2. Transfer Learning and Pretrained Models Lab (Introduction).vtt 1.9 kB
  • 8. Classification Basics/4. ROC Curve Lab Intro.vtt 1.9 kB
  • 24. ----- Reinforcement Learning -----/5. Upper Confidence Bound Lab (Intro).vtt 1.9 kB
  • 31. Recurrent Neural Networks/4. LSTM Multivariate, Multistep Timeseries Prediction (Intro).vtt 1.8 kB
  • 10. Random Forests/3. Random Forest Lab (Intro).vtt 1.8 kB
  • 31. Recurrent Neural Networks/2. LSTM Univariate, Multistep Timeseries Prediction (Intro).vtt 1.8 kB
  • 29. Autoencoders/2. Autoencoders Lab (Intro).vtt 1.8 kB
  • 9. Decision Trees/4. Decision Trees Exercise.vtt 1.7 kB
  • 15. Apriori/2. Apriori Lab (Intro).vtt 1.7 kB
  • 9. Decision Trees/2. Decision Trees Lab (Intro).vtt 1.7 kB
  • 23. Factor Analysis/5. Factor Analysis Exercise.vtt 1.6 kB
  • 2. R Refresher/2. How to get the code.vtt 1.6 kB
  • 2. R Refresher/8. Packages Preparation Lab.vtt 1.6 kB
  • 28. Convolutional Neural Networks/3. Convolutional Neural Networks Lab (Intro).vtt 1.6 kB
  • 14. ----- Association Rules -----/2. How to get the code.vtt 1.6 kB
  • 16. ----- Clustering -----/2. How to get the code.vtt 1.6 kB
  • 24. ----- Reinforcement Learning -----/4. How to get the code.vtt 1.6 kB
  • 25. ----- Deep Learning -----/10. How to get the code.vtt 1.6 kB
  • 3. ----- Regression, Model Preparation, and Regularization -----/2. How to get the code.vtt 1.6 kB
  • 7. ----- Classification -----/2. How to get the code.vtt 1.6 kB
  • 23. Factor Analysis/2. Factor Analysis Lab (Intro).vtt 1.6 kB
  • 27. Deep Learning Classification/1. Binary Classification Lab (Intro).vtt 1.5 kB
  • 26. Deep Learning Regression/1. Multi-Target Regression Lab (Intro).vtt 1.5 kB
  • 12. Support Vector Machines/2. Support Vector Machines Lab (Intro).vtt 1.5 kB
  • 11. Logistic Regression/5. Logistic Regression Exercise.vtt 1.2 kB
  • 11. Logistic Regression/2. Logistic Regression Lab (Intro).vtt 889 Bytes
  • 1. Introduction/5. Teaser Overview.vtt 573 Bytes
  • 32. Bonus/1. Congratulations and thank you.html 564 Bytes
  • 3. ----- Regression, Model Preparation, and Regularization -----/1. Section Overview.html 481 Bytes
  • 32. Bonus/2. Bonus lecture.html 417 Bytes
  • 7. ----- Classification -----/1. Classification Introduction.html 220 Bytes
  • 20. ----- Dimensionality Reduction -----/1. Dimensionality Reduction Overview.html 203 Bytes
  • 17. kmeans/4. kmeans Solution.vtt 150 Bytes
  • 13. Ensemble Models/2. Classification Quiz.html 136 Bytes
  • 19. Dbscan/3. Clustering Quiz.html 136 Bytes
  • 23. Factor Analysis/6. Dimensionality Reduction Quiz.html 136 Bytes
  • 28. Convolutional Neural Networks/9. Deep Learning Quiz.html 136 Bytes
  • 4. Regression/13. Regression Quiz.html 136 Bytes
  • [DesireCourse.Net].url 51 Bytes
  • [CourseClub.Me].url 48 Bytes

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