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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
414б |
| 001 Course structure.en.srt |
1.61Кб |
| 001 Course structure.mp4 |
9.99Мб |
| 001 Introduction to Bagging.en.srt |
1.17Кб |
| 001 Introduction to Bagging.mp4 |
7.79Мб |
| 001 Introduction to Boosting.en.srt |
1.51Кб |
| 001 Introduction to Boosting.mp4 |
9.94Мб |
| 001 Introduction to Stacking Method.en.srt |
1.06Кб |
| 001 Introduction to Stacking Method.mp4 |
6.07Мб |
| 001 Introduction to the clustering.en.srt |
1.18Кб |
| 001 Introduction to the clustering.mp4 |
7.64Мб |
| 001 Introduction to the project.en.srt |
1.46Кб |
| 001 Introduction to the project.en.srt |
1.30Кб |
| 001 Introduction to the project.mp4 |
8.86Мб |
| 001 Introduction to the project.mp4 |
8.64Мб |
| 001 Introduction to the Random Forest.en.srt |
1.46Кб |
| 001 Introduction to the Random Forest.mp4 |
8.36Мб |
| 001 Thank you.en.srt |
1.69Кб |
| 001 Thank you.mp4 |
23.29Мб |
| 001 What is ensemble learning.en.srt |
1.78Кб |
| 001 What is ensemble learning.mp4 |
12.49Мб |
| 001 What is hard and soft voting.en.srt |
7.16Кб |
| 001 What is hard and soft voting.mp4 |
40.10Мб |
| 001 What is machine learning.en.srt |
1.43Кб |
| 001 What is machine learning.mp4 |
10.13Мб |
| 002 Bootstrapping Introduction.en.srt |
2.18Кб |
| 002 Bootstrapping Introduction.mp4 |
27.98Мб |
| 002 Custom hard voting implementation Part 1.en.srt |
11.67Кб |
| 002 Custom hard voting implementation Part 1.mp4 |
158.59Мб |
| 002 Demystifying recommendation systems.en.srt |
6.77Кб |
| 002 Demystifying recommendation systems.mp4 |
48.19Мб |
| 002 Hierarchical and K-means clustering and strengths and weaknesses of K-means.en.srt |
6.56Кб |
| 002 Hierarchical and K-means clustering and strengths and weaknesses of K-means.mp4 |
48.72Мб |
| 002 How To Make The Most Out Of This Course.en.srt |
2.54Кб |
| 002 How To Make The Most Out Of This Course.mp4 |
8.20Мб |
| 002 Introduction to AdaBoost.en.srt |
6.72Кб |
| 002 Introduction to AdaBoost.mp4 |
40.26Мб |
| 002 Introduction to learning from data.en.srt |
7.20Кб |
| 002 Introduction to learning from data.mp4 |
52.50Мб |
| 002 Introduction to Meta-Learning.en.srt |
5.72Кб |
| 002 Introduction to Meta-Learning.mp4 |
39.54Мб |
| 002 Introduction to the time series.en.srt |
4.53Кб |
| 002 Introduction to the time series.mp4 |
29.05Мб |
| 002 Understanding random forest trees.en.srt |
8.80Кб |
| 002 Understanding random forest trees.mp4 |
54.21Мб |
| 002 What is bias_.en.srt |
2.76Кб |
| 002 What is bias_.mp4 |
19.62Мб |
| 003 AdaBoost Implementation Method 1.en.srt |
15.37Кб |
| 003 AdaBoost Implementation Method 1.mp4 |
192.66Мб |
| 003 Bitcoin data analysis Implementation Part 1.en.srt |
7.61Кб |
| 003 Bitcoin data analysis Implementation Part 1.mp4 |
69.86Мб |
| 003 Bootstrapping Implementation.en.srt |
7.92Кб |
| 003 Bootstrapping Implementation.mp4 |
97.37Мб |
| 003 Creating and analysing forests and strengths and weaknesses of Random Forest.en.srt |
7.22Кб |
| 003 Creating and analysing forests and strengths and weaknesses of Random Forest.mp4 |
49.37Мб |
| 003 Custom hard voting implementation Part 2.en.srt |
3.73Кб |
| 003 Custom hard voting implementation Part 2.mp4 |
55.18Мб |
| 003 K-means Implementation Part 1.en.srt |
7.51Кб |
| 003 K-means Implementation Part 1.mp4 |
97.90Мб |
| 003 Neural recommendation systems.en.srt |
4.26Кб |
| 003 Neural recommendation systems.mp4 |
24.83Мб |
| 003 Selecting base learners and meta-learner.en.srt |
5.54Кб |
| 003 Selecting base learners and meta-learner.mp4 |
42.33Мб |
| 003 Some popular machine learning dataset.en.srt |
4.87Кб |
| 003 Some popular machine learning dataset.mp4 |
31.66Мб |
| 003 What is variance and Trade-off_.en.srt |
6.14Кб |
| 003 What is variance and Trade-off_.mp4 |
39.11Мб |
| 003 Who is this course for____.en.srt |
1.79Кб |
| 003 Who is this course for____.mp4 |
10.72Мб |
| 004 AdaBoost Implementation Method 2 for classification.en.srt |
9.43Кб |
| 004 AdaBoost Implementation Method 2 for classification.mp4 |
110.00Мб |
| 004 Analysing our results.en.srt |
9.16Кб |
| 004 Analysing our results.mp4 |
120.60Мб |
| 004 Bitcoin data analysis Implementation Part 2.en.srt |
7.40Кб |
| 004 Bitcoin data analysis Implementation Part 2.mp4 |
41.41Мб |
| 004 Creating base learners for bagging.en.srt |
2.51Кб |
| 004 Creating base learners for bagging.mp4 |
15.49Мб |
| 004 Exploratory analysis.en.srt |
5.29Кб |
| 004 Exploratory analysis.mp4 |
55.65Мб |
| 004 IMPORTANT term.en.srt |
11.82Кб |
| 004 IMPORTANT term.mp4 |
105.89Мб |
| 004 K-means Implementation Part 2.en.srt |
8.03Кб |
| 004 K-means Implementation Part 2.mp4 |
129.81Мб |
| 004 Random forests Implementation for classification.en.srt |
6.35Кб |
| 004 Random forests Implementation for classification.mp4 |
74.76Мб |
| 004 Stacking for regression Implementation.en.srt |
22.12Кб |
| 004 Stacking for regression Implementation.mp4 |
276.32Мб |
| 004 What is Motivation_.en.srt |
5.13Кб |
| 004 What is Motivation_.mp4 |
32.75Мб |
| 004 What is Supervised learning_.en.srt |
5.25Кб |
| 004 What is Supervised learning_.mp4 |
36.41Мб |
| 005 AdaBoost Implementation Method 2 for Regression Solution.en.srt |
4.19Кб |
| 005 AdaBoost Implementation Method 2 for Regression Solution.mp4 |
41.03Мб |
| 005 Creating the dot model.en.srt |
16.82Кб |
| 005 Creating the dot model.mp4 |
197.64Мб |
| 005 Hard voting implementation by Using scikit-learn.en.srt |
8.74Кб |
| 005 Hard voting implementation by Using scikit-learn.mp4 |
105.25Мб |
| 005 IMPORTANT NOTE on tools.en.srt |
2.27Кб |
| 005 IMPORTANT NOTE on tools.mp4 |
5.27Мб |
| 005 K-Means Implementation by using Voting.en.srt |
8.84Кб |
| 005 K-Means Implementation by using Voting.mp4 |
93.00Мб |
| 005 Random forests Implementation for regression.en.srt |
6.21Кб |
| 005 Random forests Implementation for regression.mp4 |
83.08Мб |
| 005 Simple Bitcoin Prediction.en.srt |
14.31Кб |
| 005 Simple Bitcoin Prediction.mp4 |
179.20Мб |
| 005 Stacking for classification Implementation.en.srt |
18.75Кб |
| 005 Stacking for classification Implementation.mp4 |
226.37Мб |
| 005 Strengths and weaknesses of bagging.en.srt |
2.51Кб |
| 005 Strengths and weaknesses of bagging.mp4 |
15.51Мб |
| 005 Validation Curves Implementation.en.srt |
15.28Кб |
| 005 Validation Curves Implementation.mp4 |
197.43Мб |
| 005 What is Unsupervised learning and Dimensionality reduction_.en.srt |
5.15Кб |
| 005 What is Unsupervised learning and Dimensionality reduction_.mp4 |
35.22Мб |
| 006 Bagging Implementation Method 1.en.srt |
12.72Кб |
| 006 Bagging Implementation Method 1.mp4 |
134.70Мб |
| 006 Creating the dense model.en.srt |
7.36Кб |
| 006 Creating the dense model.mp4 |
104.95Мб |
| 006 Extra trees Implementation for classification.en.srt |
2.30Кб |
| 006 Extra trees Implementation for classification.mp4 |
30.30Мб |
| 006 How to measure performance.en.srt |
15.07Кб |
| 006 How to measure performance.mp4 |
101.56Мб |
| 006 Learning Curves Implementation.en.srt |
14.31Кб |
| 006 Learning Curves Implementation.mp4 |
194.03Мб |
| 006 Simulator Implementation.en.srt |
11.23Кб |
| 006 Simulator Implementation.mp4 |
146.55Мб |
| 006 Soft voting implementation by Using scikit-learn.en.srt |
11.89Кб |
| 006 Soft voting implementation by Using scikit-learn.mp4 |
145.82Мб |
| 006 Strengths and weaknesses of AdaBoost.en.srt |
2.21Кб |
| 006 Strengths and weaknesses of AdaBoost.mp4 |
14.93Мб |
| 006 Summary of the section.en.srt |
2.70Кб |
| 006 Summary of the section.en.srt |
1.46Кб |
| 006 Summary of the section.mp4 |
18.83Мб |
| 006 Summary of the section.mp4 |
9.25Мб |
| 007 Analysing our results.en.srt |
14.27Кб |
| 007 Analysing our results.mp4 |
208.29Мб |
| 007 Bagging Implementation Method 2 for classification.en.srt |
7.87Кб |
| 007 Bagging Implementation Method 2 for classification.mp4 |
95.10Мб |
| 007 Creating a stacking ensemble.en.srt |
10.37Кб |
| 007 Creating a stacking ensemble.mp4 |
149.97Мб |
| 007 Extra trees Implementation for regression.en.srt |
4.72Кб |
| 007 Extra trees Implementation for regression.mp4 |
52.40Мб |
| 007 Introduction to Gradient boosting.en.srt |
6.61Кб |
| 007 Introduction to Gradient boosting.mp4 |
115.88Мб |
| 007 Linear Regression Implementation.en.srt |
9.35Кб |
| 007 Linear Regression Implementation.mp4 |
109.20Мб |
| 007 Methods of Ensemble Learning.en.srt |
3.76Кб |
| 007 Methods of Ensemble Learning.mp4 |
28.49Мб |
| 007 Voting Implementation.en.srt |
11.64Кб |
| 007 Voting Implementation.mp4 |
131.92Мб |
| 008 Bagging Implementation Method 2 for regression.en.srt |
9.38Кб |
| 008 Bagging Implementation Method 2 for regression.mp4 |
113.94Мб |
| 008 Challenges in Ensemble Learning.en.srt |
8.60Кб |
| 008 Challenges in Ensemble Learning.mp4 |
52.63Мб |
| 008 Gradient boosting Implementation Method 1.en.srt |
12.59Кб |
| 008 Gradient boosting Implementation Method 1.mp4 |
160.30Мб |
| 008 Logistic Regression Implementation.en.srt |
7.98Кб |
| 008 Logistic Regression Implementation.mp4 |
79.13Мб |
| 008 Stacking Implementation.en.srt |
15.08Кб |
| 008 Stacking Implementation.mp4 |
198.23Мб |
| 008 Summary.en.srt |
2.67Кб |
| 008 Summary.en.srt |
3.06Кб |
| 008 Summary.mp4 |
19.54Мб |
| 008 Summary.mp4 |
19.37Мб |
| 008 Summary of the section.en.srt |
3.48Кб |
| 008 Summary of the section.mp4 |
25.09Мб |
| 009 Bagging Implementation.en.srt |
5.60Кб |
| 009 Bagging Implementation.mp4 |
63.75Мб |
| 009 Gradient boosting Implementation Method 2 For Regression Problem.en.srt |
7.42Кб |
| 009 Gradient boosting Implementation Method 2 For Regression Problem.mp4 |
76.34Мб |
| 009 Summary of the section.en.srt |
3.75Кб |
| 009 Summary of the section.en.srt |
3.18Кб |
| 009 Summary of the section.mp4 |
31.94Мб |
| 009 Summary of the section.mp4 |
21.95Мб |
| 009 Support vector machines.en.srt |
3.49Кб |
| 009 Support vector machines.mp4 |
25.35Мб |
| 010 Boosting Implementation.en.srt |
5.94Кб |
| 010 Boosting Implementation.mp4 |
78.59Мб |
| 010 Gradient boosting Implementation Method 2 For Classification Problem.en.srt |
4.52Кб |
| 010 Gradient boosting Implementation Method 2 For Classification Problem.mp4 |
29.64Мб |
| 010 What is Neural networks.en.srt |
5.92Кб |
| 010 What is Neural networks.mp4 |
43.31Мб |
| 011 Random Forest Implementation.en.srt |
6.37Кб |
| 011 Random Forest Implementation.mp4 |
66.66Мб |
| 011 What is Decision trees.en.srt |
6.00Кб |
| 011 What is Decision trees.mp4 |
36.27Мб |
| 011 XGBoost Introduction and Implementation for Regression.en.srt |
9.17Кб |
| 011 XGBoost Introduction and Implementation for Regression.mp4 |
112.42Мб |
| 012 Summary of the project.en.srt |
2.12Кб |
| 012 Summary of the project.mp4 |
14.03Мб |
| 012 Udemy_Linear_Regression_Model_Implementation.ipynb |
4.19Кб |
| 012 What is K-Nearest Neighbors.en.srt |
1.80Кб |
| 012 What is K-Nearest Neighbors.mp4 |
12.54Мб |
| 012 XGBoost Introduction and Implementation for Classification.en.srt |
4.28Кб |
| 012 XGBoost Introduction and Implementation for Classification.mp4 |
46.48Мб |
| 013 K-means Implementation.en.srt |
12.13Кб |
| 013 K-means Implementation.mp4 |
149.29Мб |
| 013 Summary.en.srt |
3.61Кб |
| 013 Summary.mp4 |
23.28Мб |
| 013 Udemy_Logistic_Regression_Implementation.ipynb |
5.46Кб |
| 014 Summary of the section.en.srt |
1.21Кб |
| 014 Summary of the section.mp4 |
5.10Мб |
| 018 Udemy_K_Means_Implementation.ipynb |
29.98Кб |
| 024 Validation_Curves_Implementation.ipynb |
104.25Кб |
| 025 Udemy_Learning_Curves_Implementation.ipynb |
34.10Кб |
| 030 Udemy_Hard_Voting_Implementation.ipynb |
7.09Кб |
| 031 Udemy_Hard_Voting_Implementation (1).ipynb |
9.04Кб |
| 032 Udemy_Hard_Voting_Implementation (2).ipynb |
28.88Кб |
| 033 Udemy_Hard_voting_implementation_by_Using_scikit_learn.ipynb |
6.87Кб |
| 034 Udemy_Soft_voting_implementation_by_Using_scikit_learn.ipynb |
6.66Кб |
| 035 Udemy_Soft_voting_implementation_by_Using_scikit_learn.ipynb |
27.59Кб |
| 040 Udemy_Stacking_for_regression_Implementation.ipynb |
9.38Кб |
| 041 Udemy_Stacking_for_classification_Implementation.ipynb |
8.65Кб |
| 045 Udemy_Bootstrapping_Implementation.ipynb |
16.98Кб |
| 048 Udemy_Bagging_implementatio_Method_1.ipynb |
7.33Кб |
| 049 Udemy_Bagging_implementation_Method_2_for_classification.ipynb |
4.64Кб |
| 050 Udemy_Bagging_implementation_Method_2_for_regression.ipynb |
4.87Кб |
| 054 Udemy_AdaBoosting_Implementation.ipynb |
7.51Кб |
| 055 Udemy_AdaBoost_Method_2_Implementation_for_classification.ipynb |
6.22Кб |
| 056 Udemy_AdaBoost_Method_2_Implementation_for_Regression.ipynb |
3.48Кб |
| 058 Udemy_Gradient_Boosting_Introduction_and_implementation.ipynb |
3.56Кб |
| 059 Udemy_Gradient_Boosting_Introduction_and_implementation (1).ipynb |
9.76Кб |
| 061 Udemy_Gradient_Boosting_implementation_Method_2_for_Classification.ipynb |
3.20Кб |
| 062 Udemy_XGBoost_Implementation_for_Regression.ipynb |
4.57Кб |
| 063 Udemy_XGBoost_Implementation_for_Classification.ipynb |
3.96Кб |
| 068 Udemy_Random_forests_Implementation_for_classification.ipynb |
5.14Кб |
| 069 Udemy_Random_forests_Implementation_for_Regression.ipynb |
5.54Кб |
| 070 Udemy_Extra_Trees_Implementation_for_classification.ipynb |
4.72Кб |
| 071 Udemy_Extra_Trees_Implementation_for_Regression.ipynb |
4.66Кб |
| 076 Udemy_K_Means_Clustering_Implementation_with_Scikit_Learn.ipynb |
60.91Кб |
| 077 Udemy_Voting_Example_Implementation.ipynb |
23.74Кб |
| 081 Bitcoin_data_analysis.ipynb |
79.55Кб |
| 081 BTC-USD.csv |
30.92Кб |
| 083 Udemy_Simple_Bitcoin_Prediction_Implementation.ipynb |
29.07Кб |
| 084 Udemy_Simulator_Implemetation.ipynb |
4.86Кб |
| 085 Udemy_Voting_Implementation_for_bitcoin_price_Prediction.ipynb |
28.87Кб |
| 086 Udemy_Stacking_Implementation_for_bitcoin_price_Prediction.ipynb |
30.14Кб |
| 087 Udemy_Bagging_Implementation_for_bitcoin_price_Prediction.ipynb |
25.17Кб |
| 088 Udemy_Boosting_Implementation_for_bitcoin_price_Prediction.ipynb |
55.39Кб |
| 089 Udemy_Random_Forest_Implementation_for_bitcoin_price_Prediction.ipynb |
25.30Кб |
| 094 Udemy_Exploratory_data_for_Movie_Recommendation_system.ipynb |
13.52Кб |
| 095 Creating_a_dot_model_for_Movie_Recommendation_system.ipynb |
13.69Кб |
| 096 Creating_a_dense_model_for_Movie_Recommendation_system.ipynb |
14.04Кб |
| 097 Creating_a_stacking_ensemble_for_Movie_Recommendation_system.ipynb |
14.95Кб |
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| TutsNode.com.txt |
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