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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
2.79Кб |
| 001 Regression Intro.en.srt |
25.10Кб |
| 001 Regression Intro.mp4 |
22.98Мб |
| 001 Setup & Installation.en.srt |
14.74Кб |
| 001 Setup & Installation.mp4 |
79.19Мб |
| 001 Supervised Learning Intro.en.srt |
10.54Кб |
| 001 Supervised Learning Intro.mp4 |
9.41Мб |
| 002 Classification Intro.en.srt |
10.07Кб |
| 002 Classification Intro.mp4 |
20.48Мб |
| 002 Linear Regression Practical.en.srt |
29.49Кб |
| 002 Linear Regression Practical.mp4 |
142.48Мб |
| 002 Loading Datasets.en.srt |
12.70Кб |
| 002 Loading Datasets.mp4 |
15.82Мб |
| 003 Data Format.en.srt |
11.82Кб |
| 003 Data Format.mp4 |
35.45Мб |
| 003 Logistic Regression Theory.en.srt |
27.86Кб |
| 003 Logistic Regression Theory.mp4 |
56.33Мб |
| 003 Regularized Linear Regression Practical.en.srt |
33.74Кб |
| 003 Regularized Linear Regression Practical.mp4 |
140.67Мб |
| 004 Boston Housing Intro.en.srt |
27.22Кб |
| 004 Boston Housing Intro.mp4 |
166.06Мб |
| 004 Gradient Descent.en.srt |
16.86Кб |
| 004 Gradient Descent.mp4 |
37.93Мб |
| 004 Train Test Splitting.en.srt |
20.58Кб |
| 004 Train Test Splitting.mp4 |
80.63Мб |
| 005 Polynomial Regression.en.srt |
26.33Кб |
| 005 Polynomial Regression.mp4 |
121.95Мб |
| 005 Stratified Splitting.en.srt |
19.39Кб |
| 005 Stratified Splitting.mp4 |
86.67Мб |
| 005 Types of Classification Problems.en.srt |
19.07Кб |
| 005 Types of Classification Problems.mp4 |
44.07Мб |
| 006 Creating and Training a Binary Classifier.en.srt |
36.72Кб |
| 006 Creating and Training a Binary Classifier.mp4 |
212.03Мб |
| 006 Data Preparation and Exploration.en.srt |
34.58Кб |
| 006 Data Preparation and Exploration.mp4 |
185.10Мб |
| 006 Regression Losses and Learning Rates.en.srt |
17.38Кб |
| 006 Regression Losses and Learning Rates.mp4 |
14.36Мб |
| 007 Creating and Training a Multiclass Classifier.en.srt |
17.45Кб |
| 007 Creating and Training a Multiclass Classifier.mp4 |
99.91Мб |
| 007 SGD Regression.en.srt |
32.14Кб |
| 007 SGD Regression.mp4 |
177.57Мб |
| 008 Evaluating Classifiers Theory.en.srt |
15.53Кб |
| 008 Evaluating Classifiers Theory.mp4 |
30.94Мб |
| 008 KNN Regression Theory.en.srt |
4.71Кб |
| 008 KNN Regression Theory.mp4 |
12.03Мб |
| 009 KNN Regression Practical.en.srt |
11.83Кб |
| 009 KNN Regression Practical.mp4 |
83.25Мб |
| 009 Precision and Recall Theory.en.srt |
25.05Кб |
| 009 Precision and Recall Theory.mp4 |
53.03Мб |
| 010 ROC, Confusion Matrix, and Support Theory.en.srt |
8.55Кб |
| 010 ROC, Confusion Matrix, and Support Theory.mp4 |
17.96Мб |
| 010 SVM Regression Theory.en.srt |
7.63Кб |
| 010 SVM Regression Theory.mp4 |
21.69Мб |
| 011 MNIST Data Set Intro.en.srt |
9.46Кб |
| 011 MNIST Data Set Intro.mp4 |
28.59Мб |
| 011 SVM Regression Practical.en.srt |
13.41Кб |
| 011 SVM Regression Practical.mp4 |
85.83Мб |
| 012 Decision Tree Regression Theory.en.srt |
9.61Кб |
| 012 Decision Tree Regression Theory.mp4 |
20.54Мб |
| 012 Evaluating Classifiers Practical.en.srt |
23.55Кб |
| 012 Evaluating Classifiers Practical.mp4 |
115.80Мб |
| 013 Decision Tree and Random Forest Regression Practical.en.srt |
17.30Кб |
| 013 Decision Tree and Random Forest Regression Practical.mp4 |
101.25Мб |
| 013 Validation Set.en.srt |
6.76Кб |
| 013 Validation Set.mp4 |
33.46Мб |
| 014 Additional Regression Metrics.en.srt |
10.41Кб |
| 014 Additional Regression Metrics.mp4 |
16.82Мб |
| 014 Cross-Validation.en.srt |
36.91Кб |
| 014 Cross-Validation.mp4 |
162.79Мб |
| 015 Ensembles Intro.en.srt |
8.47Кб |
| 015 Ensembles Intro.mp4 |
17.75Мб |
| 015 Hyperparameters.en.srt |
21.76Кб |
| 015 Hyperparameters.mp4 |
103.18Мб |
| 016 Regularization Theory.en.srt |
25.55Кб |
| 016 Regularization Theory.mp4 |
60.57Мб |
| 016 Voting Ensembles Theory.en.srt |
9.89Кб |
| 016 Voting Ensembles Theory.mp4 |
19.95Мб |
| 017 Generalization Error Sources.en.srt |
20.10Кб |
| 017 Generalization Error Sources.mp4 |
18.05Мб |
| 017 Voting Classification Practical.en.srt |
18.73Кб |
| 017 Voting Classification Practical.mp4 |
89.45Мб |
| 018 Regularization Practical.en.srt |
13.29Кб |
| 018 Regularization Practical.mp4 |
75.55Мб |
| 018 Voting Regression Practical.en.srt |
6.22Кб |
| 018 Voting Regression Practical.mp4 |
30.41Мб |
| 019 Bagging and Pasting Theory.en.srt |
14.38Кб |
| 019 Bagging and Pasting Theory.mp4 |
33.06Мб |
| 019 Grid and Randomized Search.en.srt |
46.53Кб |
| 019 Grid and Randomized Search.mp4 |
283.66Мб |
| 020 Bagging and Pasting Classification Practical.en.srt |
14.21Кб |
| 020 Bagging and Pasting Classification Practical.mp4 |
71.07Мб |
| 020 Handling Missing Values.en.srt |
46.92Кб |
| 020 Handling Missing Values.mp4 |
255.98Мб |
| 021 Bagging and Pasting Regression Practical.en.srt |
11.60Кб |
| 021 Bagging and Pasting Regression Practical.mp4 |
60.35Мб |
| 021 Feature Scaling Theory.en.srt |
31.18Кб |
| 021 Feature Scaling Theory.mp4 |
62.30Мб |
| 022 AdaBoost Theory.en.srt |
16.55Кб |
| 022 AdaBoost Theory.mp4 |
35.04Мб |
| 022 Feature Scaling Practical.en.srt |
31.60Кб |
| 022 Feature Scaling Practical.mp4 |
162.01Мб |
| 023 AdaBoost Classification Practical.en.srt |
18.50Кб |
| 023 AdaBoost Classification Practical.mp4 |
112.87Мб |
| 023 Text and Categorical Data.en.srt |
51.23Кб |
| 023 Text and Categorical Data.mp4 |
301.01Мб |
| 024 AdaBoost Regression Practical.en.srt |
6.60Кб |
| 024 AdaBoost Regression Practical.mp4 |
35.16Мб |
| 024 Transformation Pipelines.en.srt |
21.86Кб |
| 024 Transformation Pipelines.mp4 |
97.62Мб |
| 025 Custom Transformers.en.srt |
14.37Кб |
| 025 Custom Transformers.mp4 |
21.70Мб |
| 025 Gradient Boosting Theory.en.srt |
18.64Кб |
| 025 Gradient Boosting Theory.mp4 |
45.71Мб |
| 026 Column Specific Pipelines.en.srt |
21.66Кб |
| 026 Column Specific Pipelines.mp4 |
122.59Мб |
| 026 Gradient Boosting Classification Pratical.en.srt |
13.30Кб |
| 026 Gradient Boosting Classification Pratical.mp4 |
73.77Мб |
| 027 Gradient Boosting Regression Practical.en.srt |
10.44Кб |
| 027 Gradient Boosting Regression Practical.mp4 |
57.80Мб |
| 027 Over and Undersampling.en.srt |
62.50Кб |
| 027 Over and Undersampling.mp4 |
279.74Мб |
| 028 Feature Importance.en.srt |
47.84Кб |
| 028 Feature Importance.mp4 |
224.44Мб |
| 028 Stacking and Blending Theory.en.srt |
11.38Кб |
| 028 Stacking and Blending Theory.mp4 |
26.18Мб |
| 029 Saving and Loading Models and Pipelines.en.srt |
22.56Кб |
| 029 Saving and Loading Models and Pipelines.mp4 |
113.84Мб |
| 029 Stacking Classifiers Practical.en.srt |
20.19Кб |
| 029 Stacking Classifiers Practical.mp4 |
107.77Мб |
| 030 Post Prototyping.en.srt |
35.06Кб |
| 030 Post Prototyping.mp4 |
86.18Мб |
| 030 Stacking Regression Practical.en.srt |
10.41Кб |
| 030 Stacking Regression Practical.mp4 |
58.21Мб |
| 031 Dimensionality Reduction Intro.en.srt |
25.38Кб |
| 031 Dimensionality Reduction Intro.mp4 |
80.37Мб |
| 031 Multilabel Classification.en.srt |
24.50Кб |
| 031 Multilabel Classification.mp4 |
152.98Мб |
| 032 PCA Theory.en.srt |
43.72Кб |
| 032 PCA Theory.mp4 |
114.42Мб |
| 032 Polynomial Features.en.srt |
21.92Кб |
| 032 Polynomial Features.mp4 |
97.64Мб |
| 033 PCA Practical.en.srt |
42.17Кб |
| 033 PCA Practical.mp4 |
220.99Мб |
| 033 SVM Theory.en.srt |
59.06Кб |
| 033 SVM Theory.mp4 |
141.35Мб |
| 034 NNMF Theory.en.srt |
10.68Кб |
| 034 NNMF Theory.mp4 |
21.48Мб |
| 034 SVM Classification Practical.en.srt |
47.11Кб |
| 034 SVM Classification Practical.mp4 |
266.01Мб |
| 035 KNN Classification Theory.en.srt |
26.20Кб |
| 035 KNN Classification Theory.mp4 |
58.81Мб |
| 035 NNMF Practical.en.srt |
23.78Кб |
| 035 NNMF Practical.mp4 |
114.14Мб |
| 036 Isomap Theory.en.srt |
7.00Кб |
| 036 Isomap Theory.mp4 |
14.18Мб |
| 036 KNN Classification Practical.en.srt |
21.74Кб |
| 036 KNN Classification Practical.mp4 |
111.56Мб |
| 037 Decision Tree Classifier Theory.en.srt |
49.33Кб |
| 037 Decision Tree Classifier Theory.mp4 |
163.50Мб |
| 037 Isomap Practical.en.srt |
24.86Кб |
| 037 Isomap Practical.mp4 |
120.87Мб |
| 038 Decision Tree Pruning.en.srt |
5.03Кб |
| 038 Decision Tree Pruning.mp4 |
5.78Мб |
| 038 LLE Theory.en.srt |
16.17Кб |
| 038 LLE Theory.mp4 |
39.73Мб |
| 039 Decision Tree Practical.en.srt |
22.30Кб |
| 039 Decision Tree Practical.mp4 |
108.49Мб |
| 039 LLE Practical.en.srt |
18.84Кб |
| 039 LLE Practical.mp4 |
111.13Мб |
| 040 Random Forest Theory.en.srt |
12.57Кб |
| 040 Random Forest Theory.mp4 |
53.90Мб |
| 040 t-SNE Theory.en.srt |
24.85Кб |
| 040 t-SNE Theory.mp4 |
66.26Мб |
| 041 Random Forest Practical.en.srt |
13.85Кб |
| 041 Random Forest Practical.mp4 |
65.82Мб |
| 041 t-SNE Practical.en.srt |
21.76Кб |
| 041 t-SNE Practical.mp4 |
113.48Мб |
| 042 Naive Bayes Theory.en.srt |
13.11Кб |
| 042 Naive Bayes Theory.mp4 |
26.35Мб |
| 042 Unsupervised Learning Intro.en.srt |
10.97Кб |
| 042 Unsupervised Learning Intro.mp4 |
19.28Мб |
| 043 KMeans Theory.en.srt |
16.43Кб |
| 043 KMeans Theory.mp4 |
31.67Мб |
| 043 Naive Bayes Practical.en.srt |
12.45Кб |
| 043 Naive Bayes Practical.mp4 |
63.73Мб |
| 044 How to Choose a Model.en.srt |
17.18Кб |
| 044 How to Choose a Model.mp4 |
46.08Мб |
| 044 KMeans Practical.en.srt |
25.36Кб |
| 044 KMeans Practical.mp4 |
126.27Мб |
| 045 Choosing Number of Clusters Theory.en.srt |
29.88Кб |
| 045 Choosing Number of Clusters Theory.mp4 |
64.35Мб |
| 046 Choosing Number of Clusters Practical.en.srt |
16.46Кб |
| 046 Choosing Number of Clusters Practical.mp4 |
112.91Мб |
| 047 DBSCAN Theory.en.srt |
15.61Кб |
| 047 DBSCAN Theory.mp4 |
33.55Мб |
| 048 DBSCAN Practical.en.srt |
14.80Кб |
| 048 DBSCAN Practical.mp4 |
61.40Мб |
| 049 Gaussian Mixture Theory.en.srt |
23.74Кб |
| 049 Gaussian Mixture Theory.mp4 |
48.92Мб |
| 050 Gaussian Mixture Practical.en.srt |
23.69Кб |
| 050 Gaussian Mixture Practical.mp4 |
126.40Мб |
| 051 Semi-Supervised Theory.en.srt |
16.91Кб |
| 051 Semi-Supervised Theory.mp4 |
14.83Мб |
| 052 Semi-Supervised Practical.en.srt |
19.15Кб |
| 052 Semi-Supervised Practical.mp4 |
96.44Мб |
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| TutsNode.com.txt |
63б |