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

Creation Time	Jan. 3, 2024, 5:56 a.m.
Last Access Time	Nov. 21, 2024, 6:34 a.m.
File Size	7.3 GB
Keywords	with DesireCourse - Deep Udemy Machine R Learning Net incl
Total Requests	52
Total Files	298
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28. Convolutional Neural Networks/4. Convolutional Neural Networks Lab (Coding).mp4 189.8 MB
6. Regularization/2. Regularization Lab.mp4 189.6 MB
18. Hierarchical Clustering/3. Hierarchical Clustering Lab.mp4 189.2 MB
5. Model Preparation and Evaluation/6. Resampling Techniques Lab.mp4 188.6 MB
17. kmeans/2. kmeans Lab.mp4 159.9 MB
31. Recurrent Neural Networks/3. LSTM Univariate, Multistep Timeseries Prediction (Coding).mp4 146.6 MB
31. Recurrent Neural Networks/5. LSTM Multivariate, Multistep Timeseries Prediction (Coding).mp4 141.5 MB
24. ----- Reinforcement Learning -----/6. Upper Confidence Bound Lab (Coding 12).mp4 138.3 MB
4. Regression/10. Multivariate Regression Lab.mp4 135.7 MB
8. Classification Basics/7. ROC Curve Lab 33 (ROC, AUC, Cost Function).mp4 135.5 MB
27. Deep Learning Classification/6. Multi-Label Classification Lab (Coding 23).mp4 128.8 MB
21. Principal Component Analysis (PCA)/2. PCA Lab.mp4 127.0 MB
1. Introduction/6. Teaser Lab.mp4 126.5 MB
4. Regression/12. Multivariate Regression Solution.mp4 122.6 MB
27. Deep Learning Classification/2. Binary Classification Lab (Coding 12).mp4 121.0 MB
9. Decision Trees/3. Decision Trees Lab (Coding).mp4 121.0 MB
26. Deep Learning Regression/2. Multi-Target Regression Lab (Coding 12).mp4 119.1 MB
8. Classification Basics/5. ROC Curve Lab 13 (Data Prep, Modeling).mp4 118.1 MB
4. Regression/8. Polynomial Regression Lab.mp4 117.6 MB
5. Model Preparation and Evaluation/4. Train Validation Test Split Lab.mp4 117.4 MB
15. Apriori/4. Apriori Lab (Coding 22).mp4 113.4 MB
19. Dbscan/2. Dbscan Lab.mp4 111.4 MB
10. Random Forests/4. Random Forest Lab (Coding 12).mp4 109.9 MB
27. Deep Learning Classification/5. Multi-Label Classification Lab (Coding 13).mp4 109.7 MB
10. Random Forests/5. Random Forest Lab (Coding 22).mp4 107.1 MB
17. kmeans/4. kmeans Solution.mp4 106.4 MB
29. Autoencoders/3. Autoencoders Lab (Coding).mp4 105.4 MB
2. R Refresher/5. Data Manipulation Lab.mp4 104.6 MB
2. R Refresher/7. Data Reshaping Lab.mp4 103.1 MB
2. R Refresher/1. R and RStudio Installation.mp4 102.3 MB
15. Apriori/6. Apriori Solution.mp4 100.1 MB
30. Transfer Learning and Pretrained Models/3. Transfer Learning and Pretrained Models Lab (Coding).mp4 99.4 MB
26. Deep Learning Regression/3. Multi-Target Regression Lab (Coding 22).mp4 98.8 MB
11. Logistic Regression/3. Logistic Regression Lab (Coding 12).mp4 91.9 MB
23. Factor Analysis/4. Factor Analysis Lab (Coding 22).mp4 91.7 MB
4. Regression/4. Univariate Regression Lab.mp4 88.4 MB
21. Principal Component Analysis (PCA)/4. PCA Solution.mp4 81.0 MB
12. Support Vector Machines/3. Support Vector Machines Lab (Coding 12).mp4 78.8 MB
23. Factor Analysis/3. Factor Analysis Lab (Coding 12).mp4 78.7 MB
15. Apriori/3. Apriori Lab (Coding 12).mp4 73.3 MB
25. ----- Deep Learning -----/11. Python and Keras Installation.mp4 72.7 MB
4. Regression/6. Univariate Regression Solution.mp4 71.3 MB
8. Classification Basics/6. ROC Curve Lab 23 (Confusion Matrix and ROC).mp4 70.8 MB
22. t-SNE/3. t-SNE Lab (Mnist).mp4 70.4 MB
27. Deep Learning Classification/3. Binary Classification Lab (Coding 22).mp4 67.9 MB
24. ----- Reinforcement Learning -----/7. Upper Confidence Bound Lab (Coding 22).mp4 67.1 MB
2. R Refresher/3. Rmarkdown Lab.mp4 65.8 MB
11. Logistic Regression/4. Logistic Regression Lab (Coding 22).mp4 63.1 MB
27. Deep Learning Classification/7. Multi-Label Classification Lab (Coding 33).mp4 62.7 MB
28. Convolutional Neural Networks/6. Semantic Segmentation 101.mp4 57.9 MB
22. t-SNE/2. t-SNE Lab (Sphere).mp4 57.4 MB
5. Model Preparation and Evaluation/1. Underfitting Overfitting 101.mp4 56.1 MB
24. ----- Reinforcement Learning -----/2. Upper Confidence Bound 101.mp4 50.4 MB
8. Classification Basics/2. ROC Curve 101.mp4 48.0 MB
24. ----- Reinforcement Learning -----/3. Upper Confidence Bound Interactive.mp4 46.5 MB
28. Convolutional Neural Networks/1. Convolutional Neural Networks 101.mp4 44.3 MB
8. Classification Basics/3. ROC Curve Interactive.mp4 43.5 MB
12. Support Vector Machines/4. Support Vector Machines Lab (Coding 22).mp4 42.1 MB
21. Principal Component Analysis (PCA)/1. PCA 101.mp4 41.8 MB
24. ----- Reinforcement Learning -----/1. Reinforcement Learning 101.mp4 37.4 MB
5. Model Preparation and Evaluation/3. Train Validation Test Split Interactive.mp4 36.0 MB
23. Factor Analysis/1. Factor Analysis 101.mp4 35.0 MB
18. Hierarchical Clustering/2. Hierarchical Clustering Interactive.mp4 34.1 MB
30. Transfer Learning and Pretrained Models/1. Transfer Learning and Pretrained Models 101.mp4 32.8 MB
18. Hierarchical Clustering/1. Hierarchical Clustering 101.mp4 32.4 MB
17. kmeans/1. kmeans 101.mp4 31.7 MB
19. Dbscan/1. Dbscan 101.mp4 31.3 MB
1. Introduction/3. Machine Learning 101.mp4 31.1 MB
15. Apriori/1. Apriori 101.mp4 29.9 MB
1. Introduction/2. AI 101.mp4 29.5 MB
31. Recurrent Neural Networks/1. Recurrent Neural Networks 101.mp4 29.5 MB
8. Classification Basics/1. Confusion Matrix 101.mp4 28.9 MB
1. Introduction/4. Models.mp4 27.6 MB
11. Logistic Regression/1. Logistic Regression 101.mp4 27.6 MB
17. kmeans/3. kmeans Exercise.mp4 27.5 MB
25. ----- Deep Learning -----/1. Deep Learning General Overview.mp4 26.4 MB
4. Regression/2. Univariate Regression 101.mp4 25.6 MB
28. Convolutional Neural Networks/7. Semantic Segmentation Lab (Intro).mp4 25.5 MB
28. Convolutional Neural Networks/8. Semantic Segmentation Lab (Coding).mp4 25.5 MB
27. Deep Learning Classification/4. Multi-Label Classification Lab (Intro).mp4 24.4 MB
6. Regularization/1. Regularization 101.mp4 23.8 MB
28. Convolutional Neural Networks/5. Convolutional Neural Networks Exercise.mp4 23.7 MB
4. Regression/9. Multivariate Regression 101.mp4 22.4 MB
25. ----- Deep Learning -----/8. Optimizer.mp4 22.3 MB
10. Random Forests/6. Random Forest Exercise.mp4 22.1 MB
4. Regression/3. Univariate Regression Interactive.mp4 21.9 MB
12. Support Vector Machines/1. Support Vector Machines 101.mp4 21.8 MB
25. ----- Deep Learning -----/5. Layer Types.mp4 21.7 MB
12. Support Vector Machines/5. Support Vector Machines Exercise.mp4 21.2 MB
14. ----- Association Rules -----/1. Association Rules 101.mp4 20.9 MB
25. ----- Deep Learning -----/6. Activation Functions.mp4 20.7 MB
9. Decision Trees/1. Decision Trees 101.mp4 20.5 MB
22. t-SNE/1. t-SNE 101.mp4 20.0 MB
25. ----- Deep Learning -----/4. From Perceptron to Neural Networks.mp4 19.8 MB
2. R Refresher/6. Data Reshaping 101.mp4 18.8 MB
4. Regression/5. Univariate Regression Exercise.mp4 18.2 MB
28. Convolutional Neural Networks/2. Convolutional Neural Networks Interactive.mp4 18.2 MB
15. Apriori/2. Apriori Lab (Intro).mp4 18.1 MB
4. Regression/1. Regression Types 101.mp4 17.7 MB
10. Random Forests/2. Random Forests Interactive.mp4 17.5 MB
15. Apriori/5. Apriori Exercise.mp4 17.2 MB
5. Model Preparation and Evaluation/5. Resampling Techniques 101.mp4 17.2 MB
29. Autoencoders/1. Autoencoders 101.mp4 16.7 MB
23. Factor Analysis/2. Factor Analysis Lab (Intro).mp4 16.5 MB
27. Deep Learning Classification/1. Binary Classification Lab (Intro).mp4 15.3 MB
21. Principal Component Analysis (PCA)/3. PCA Exercise.mp4 15.2 MB
29. Autoencoders/2. Autoencoders Lab (Intro).mp4 15.1 MB
10. Random Forests/3. Random Forest Lab (Intro).mp4 14.8 MB
9. Decision Trees/4. Decision Trees Exercise.mp4 14.1 MB
25. ----- Deep Learning -----/7. Loss Function.mp4 13.9 MB
4. Regression/11. Multivariate Regression Exercise.mp4 13.7 MB
30. Transfer Learning and Pretrained Models/2. Transfer Learning and Pretrained Models Lab (Introduction).mp4 13.6 MB
31. Recurrent Neural Networks/2. LSTM Univariate, Multistep Timeseries Prediction (Intro).mp4 13.6 MB
12. Support Vector Machines/2. Support Vector Machines Lab (Intro).mp4 13.6 MB
5. Model Preparation and Evaluation/2. Train Validation Test Split 101.mp4 13.5 MB
26. Deep Learning Regression/1. Multi-Target Regression Lab (Intro).mp4 13.3 MB
23. Factor Analysis/5. Factor Analysis Exercise.mp4 13.2 MB
24. ----- Reinforcement Learning -----/5. Upper Confidence Bound Lab (Intro).mp4 13.1 MB
8. Classification Basics/4. ROC Curve Lab Intro.mp4 12.6 MB
25. ----- Deep Learning -----/2. Deep Learning Modeling 101.mp4 12.4 MB
2. R Refresher/8. Packages Preparation Lab.mp4 12.3 MB
28. Convolutional Neural Networks/3. Convolutional Neural Networks Lab (Intro).mp4 12.1 MB
13. Ensemble Models/1. Ensemble Models 101.mp4 12.1 MB
31. Recurrent Neural Networks/4. LSTM Multivariate, Multistep Timeseries Prediction (Intro).mp4 12.0 MB
4. Regression/7. Polynomial Regression 101.mp4 11.3 MB
25. ----- Deep Learning -----/3. Performance.mp4 11.2 MB
10. Random Forests/1. Random Forests 101.mp4 10.8 MB
11. Logistic Regression/5. Logistic Regression Exercise.mp4 10.7 MB
9. Decision Trees/2. Decision Trees Lab (Intro).mp4 10.6 MB
1. Introduction/1. Course Overview.mp4 10.4 MB
16. ----- Clustering -----/1. Clustering Overview.mp4 10.1 MB
25. ----- Deep Learning -----/9. Deep Learning Frameworks.mp4 9.5 MB
2. R Refresher/4. Piping 101.mp4 9.4 MB
7. ----- Classification -----/2. How to get the code.mp4 8.8 MB
2. R Refresher/2. How to get the code.mp4 8.8 MB
24. ----- Reinforcement Learning -----/4. How to get the code.mp4 8.8 MB
14. ----- Association Rules -----/2. How to get the code.mp4 8.8 MB
3. ----- Regression, Model Preparation, and Regularization -----/2. How to get the code.mp4 8.8 MB
25. ----- Deep Learning -----/10. How to get the code.mp4 8.8 MB
16. ----- Clustering -----/2. How to get the code.mp4 8.8 MB
11. Logistic Regression/2. Logistic Regression Lab (Intro).mp4 8.8 MB
1. Introduction/5. Teaser Overview.mp4 6.2 MB
1. Introduction/6.2 PCA_Teaser_Final.html.html 4.9 MB
28. Convolutional Neural Networks/4. Convolutional Neural Networks Lab (Coding).vtt 15.7 KB
5. Model Preparation and Evaluation/6. Resampling Techniques Lab.vtt 14.6 KB
18. Hierarchical Clustering/3. Hierarchical Clustering Lab.vtt 14.4 KB
6. Regularization/2. Regularization Lab.vtt 13.8 KB
24. ----- Reinforcement Learning -----/2. Upper Confidence Bound 101.vtt 13.1 KB
19. Dbscan/2. Dbscan Lab.vtt 12.7 KB
9. Decision Trees/3. Decision Trees Lab (Coding).vtt 12.7 KB
17. kmeans/2. kmeans Lab.vtt 12.4 KB
1. Introduction/6. Teaser Lab.vtt 12.2 KB
5. Model Preparation and Evaluation/1. Underfitting Overfitting 101.vtt 12.2 KB
21. Principal Component Analysis (PCA)/2. PCA Lab.vtt 12.1 KB
31. Recurrent Neural Networks/3. LSTM Univariate, Multistep Timeseries Prediction (Coding).vtt 11.9 KB
4. Regression/10. Multivariate Regression Lab.vtt 11.9 KB
4. Regression/8. Polynomial Regression Lab.vtt 11.3 KB
24. ----- Reinforcement Learning -----/6. Upper Confidence Bound Lab (Coding 12).vtt 10.9 KB
31. Recurrent Neural Networks/5. LSTM Multivariate, Multistep Timeseries Prediction (Coding).vtt 10.9 KB
10. Random Forests/4. Random Forest Lab (Coding 12).vtt 10.4 KB
28. Convolutional Neural Networks/1. Convolutional Neural Networks 101.vtt 10.4 KB
2. R Refresher/7. Data Reshaping Lab.vtt 10.3 KB
5. Model Preparation and Evaluation/4. Train Validation Test Split Lab.vtt 10.1 KB
4. Regression/4. Univariate Regression Lab.vtt 10.1 KB
4. Regression/12. Multivariate Regression Solution.vtt 10.0 KB
8. Classification Basics/7. ROC Curve Lab 33 (ROC, AUC, Cost Function).vtt 10.0 KB
27. Deep Learning Classification/6. Multi-Label Classification Lab (Coding 23).vtt 9.7 KB
8. Classification Basics/5. ROC Curve Lab 13 (Data Prep, Modeling).vtt 9.7 KB
27. Deep Learning Classification/2. Binary Classification Lab (Coding 12).vtt 9.4 KB
2. R Refresher/5. Data Manipulation Lab.vtt 9.1 KB
26. Deep Learning Regression/2. Multi-Target Regression Lab (Coding 12).vtt 9.1 KB
15. Apriori/6. Apriori Solution.vtt 8.9 KB
29. Autoencoders/3. Autoencoders Lab (Coding).vtt 8.9 KB
21. Principal Component Analysis (PCA)/1. PCA 101.vtt 8.8 KB
23. Factor Analysis/1. Factor Analysis 101.vtt 8.7 KB
27. Deep Learning Classification/5. Multi-Label Classification Lab (Coding 13).vtt 8.6 KB
2. R Refresher/1. R and RStudio Installation.vtt 8.5 KB
30. Transfer Learning and Pretrained Models/3. Transfer Learning and Pretrained Models Lab (Coding).vtt 8.4 KB
10. Random Forests/5. Random Forest Lab (Coding 22).vtt 8.2 KB
2. R Refresher/3. Rmarkdown Lab.vtt 8.2 KB
24. ----- Reinforcement Learning -----/1. Reinforcement Learning 101.vtt 8.1 KB
18. Hierarchical Clustering/1. Hierarchical Clustering 101.vtt 8.1 KB
28. Convolutional Neural Networks/6. Semantic Segmentation 101.vtt 7.8 KB
1. Introduction/3. Machine Learning 101.vtt 7.7 KB
17. kmeans/1. kmeans 101.vtt 7.7 KB
26. Deep Learning Regression/3. Multi-Target Regression Lab (Coding 22).vtt 7.7 KB
31. Recurrent Neural Networks/1. Recurrent Neural Networks 101.vtt 7.6 KB
15. Apriori/4. Apriori Lab (Coding 22).vtt 7.4 KB
15. Apriori/1. Apriori 101.vtt 7.4 KB
11. Logistic Regression/1. Logistic Regression 101.vtt 7.4 KB
11. Logistic Regression/3. Logistic Regression Lab (Coding 12).vtt 7.3 KB
5. Model Preparation and Evaluation/3. Train Validation Test Split Interactive.vtt 7.3 KB
8. Classification Basics/2. ROC Curve 101.vtt 7.1 KB
23. Factor Analysis/4. Factor Analysis Lab (Coding 22).vtt 6.9 KB
24. ----- Reinforcement Learning -----/3. Upper Confidence Bound Interactive.vtt 6.9 KB
25. ----- Deep Learning -----/8. Optimizer.vtt 6.8 KB
12. Support Vector Machines/3. Support Vector Machines Lab (Coding 12).vtt 6.8 KB
21. Principal Component Analysis (PCA)/4. PCA Solution.vtt 6.8 KB
23. Factor Analysis/3. Factor Analysis Lab (Coding 12).vtt 6.5 KB
22. t-SNE/1. t-SNE 101.vtt 6.5 KB
8. Classification Basics/1. Confusion Matrix 101.vtt 6.3 KB
4. Regression/6. Univariate Regression Solution.vtt 6.3 KB
25. ----- Deep Learning -----/11. Python and Keras Installation.vtt 6.2 KB
8. Classification Basics/3. ROC Curve Interactive.vtt 6.2 KB
4. Regression/2. Univariate Regression 101.vtt 6.2 KB
6. Regularization/1. Regularization 101.vtt 6.1 KB
15. Apriori/3. Apriori Lab (Coding 12).vtt 6.1 KB
9. Decision Trees/1. Decision Trees 101.vtt 6.0 KB
1. Introduction/4. Models.vtt 5.9 KB
22. t-SNE/3. t-SNE Lab (Mnist).vtt 5.8 KB
11. Logistic Regression/4. Logistic Regression Lab (Coding 22).vtt 5.8 KB
18. Hierarchical Clustering/2. Hierarchical Clustering Interactive.vtt 5.8 KB
12. Support Vector Machines/1. Support Vector Machines 101.vtt 5.6 KB
1. Introduction/2. AI 101.vtt 5.5 KB
14. ----- Association Rules -----/1. Association Rules 101.vtt 5.5 KB
30. Transfer Learning and Pretrained Models/1. Transfer Learning and Pretrained Models 101.vtt 5.4 KB
27. Deep Learning Classification/3. Binary Classification Lab (Coding 22).vtt 5.3 KB
22. t-SNE/2. t-SNE Lab (Sphere).vtt 5.2 KB
27. Deep Learning Classification/7. Multi-Label Classification Lab (Coding 33).vtt 5.2 KB
8. Classification Basics/6. ROC Curve Lab 23 (Confusion Matrix and ROC).vtt 5.2 KB
5. Model Preparation and Evaluation/5. Resampling Techniques 101.vtt 5.1 KB
24. ----- Reinforcement Learning -----/7. Upper Confidence Bound Lab (Coding 22).vtt 5.0 KB
19. Dbscan/1. Dbscan 101.vtt 5.0 KB
4. Regression/9. Multivariate Regression 101.vtt 4.8 KB
25. ----- Deep Learning -----/2. Deep Learning Modeling 101.vtt 4.7 KB
25. ----- Deep Learning -----/6. Activation Functions.vtt 4.6 KB
25. ----- Deep Learning -----/5. Layer Types.vtt 4.6 KB
25. ----- Deep Learning -----/1. Deep Learning General Overview.vtt 4.3 KB
4. Regression/1. Regression Types 101.vtt 4.3 KB
12. Support Vector Machines/4. Support Vector Machines Lab (Coding 22).vtt 4.1 KB
4. Regression/3. Univariate Regression Interactive.vtt 4.1 KB
25. ----- Deep Learning -----/4. From Perceptron to Neural Networks.vtt 4.1 KB
25. ----- Deep Learning -----/7. Loss Function.vtt 3.8 KB
13. Ensemble Models/1. Ensemble Models 101.vtt 3.7 KB
2. R Refresher/6. Data Reshaping 101.vtt 3.5 KB
28. Convolutional Neural Networks/2. Convolutional Neural Networks Interactive.vtt 3.5 KB
10. Random Forests/2. Random Forests Interactive.vtt 3.4 KB
1. Introduction/6.1 PCA_Teaser.zip.zip 3.4 KB
1. Introduction/1. Course Overview.vtt 3.1 KB
17. kmeans/3. kmeans Exercise.vtt 3.1 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 3.0 KB
10. Random Forests/1. Random Forests 101.vtt 2.9 KB
16. ----- Clustering -----/1. Clustering Overview.vtt 2.9 KB
25. ----- Deep Learning -----/3. Performance.vtt 2.9 KB
2. R Refresher/4. Piping 101.vtt 2.9 KB
29. Autoencoders/1. Autoencoders 101.vtt 2.7 KB
28. Convolutional Neural Networks/7. Semantic Segmentation Lab (Intro).vtt 2.7 KB
28. Convolutional Neural Networks/8. Semantic Segmentation Lab (Coding).vtt 2.7 KB
25. ----- Deep Learning -----/9. Deep Learning Frameworks.vtt 2.6 KB
28. Convolutional Neural Networks/5. Convolutional Neural Networks Exercise.vtt 2.5 KB
4. Regression/7. Polynomial Regression 101.vtt 2.5 KB
10. Random Forests/6. Random Forest Exercise.vtt 2.3 KB
4. Regression/5. Univariate Regression Exercise.vtt 2.2 KB
15. Apriori/5. Apriori Exercise.vtt 2.1 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 1.8 KB
24. ----- Reinforcement Learning -----/5. Upper Confidence Bound Lab (Intro).vtt 1.8 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 1.7 KB
29. Autoencoders/2. Autoencoders Lab (Intro).vtt 1.7 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 1.6 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 1.5 KB
16. ----- Clustering -----/2. How to get the code.vtt 1.5 KB
24. ----- Reinforcement Learning -----/4. How to get the code.vtt 1.5 KB
25. ----- Deep Learning -----/10. How to get the code.vtt 1.5 KB
3. ----- Regression, Model Preparation, and Regularization -----/2. How to get the code.vtt 1.5 KB
7. ----- Classification -----/2. How to get the code.vtt 1.5 KB
23. Factor Analysis/2. Factor Analysis Lab (Intro).vtt 1.5 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

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

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