[ CoursePig.com ] Udemy - Complete Bootcamp 2021 - Feature selection using Python

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[ CoursePig.com ] Udemy - Complete Bootcamp 2021 - Feature selection using Python

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种子哈希：4ad40d8152d85bbe41f2ffeae39fc6b6cb9499e4
文件大小： 1.44G
已经下载：136次
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收录时间：2024-01-01
最近下载：2025-02-22

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文件列表

  • ~Get Your Files Here !/3. Filter Method/3. Project 1 Variance for Feature selection on data for classification.mp4 191.6 MB
  • ~Get Your Files Here !/3. Filter Method/4. Project 2 Variance for Feature selection on data for regression.mp4 123.3 MB
  • ~Get Your Files Here !/3. Filter Method/13. Project 6 Mutual information implementation on a dataset with discrete target.mp4 83.1 MB
  • ~Get Your Files Here !/3. Filter Method/9. Project 4 Feature selection using anova F-Score.mp4 81.2 MB
  • ~Get Your Files Here !/3. Filter Method/11. Project 5 To select features from a dataset using Mutual Information.mp4 71.8 MB
  • ~Get Your Files Here !/3. Filter Method/7. Project 3 Feature selection using F Score.mp4 71.3 MB
  • ~Get Your Files Here !/4. Wrapper methods/12. Project 11 Backward feature selection implementation.mp4 62.0 MB
  • ~Get Your Files Here !/4. Wrapper methods/3. Project 8 Implementation of forward feature selection using sklearn.mp4 61.3 MB
  • ~Get Your Files Here !/4. Wrapper methods/14. Project 12 Implementation of Exhaustive feature selection.mp4 60.1 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/8. Project 17 Implementation of Logistic Regression with Lasso Regularization.mp4 55.5 MB
  • ~Get Your Files Here !/4. Wrapper methods/6. Project 10 Implementation of forward feature selection mlxtend.mp4 54.1 MB
  • ~Get Your Files Here !/4. Wrapper methods/9. Project 11 Backward feature elimination implementation sklearn.mp4 48.0 MB
  • ~Get Your Files Here !/3. Filter Method/15. Project 7 Implementation of chi2.mp4 45.7 MB
  • ~Get Your Files Here !/3. Filter Method/10. Mutual information to select features in a datasets with continuous target.mp4 43.3 MB
  • ~Get Your Files Here !/3. Filter Method/6. Feature selection using F-Score.mp4 42.4 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/3. Project 13 Implementation of Embedded Method using Decision Tree Classifier.mp4 42.3 MB
  • ~Get Your Files Here !/4. Wrapper methods/2. Forward Feature Selection.mp4 37.2 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/5. Project 15 Implementation of Embedded Method using Extremely randomized trees.mp4 36.2 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/4. Project 14 Implementation of Embedded Method using RandomForest Regressor.mp4 35.2 MB
  • ~Get Your Files Here !/4. Wrapper methods/4. Project 9 Implementation of forward feature selection using sklearn.mp4 30.8 MB
  • ~Get Your Files Here !/3. Filter Method/14. Chi2 test method to select feature.mp4 28.2 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/7. Project 16 Implementation of Lasso Regularization.mp4 27.3 MB
  • ~Get Your Files Here !/3. Filter Method/5. Project 2 Variance for Feature selection on data for regression part 2.mp4 26.9 MB
  • ~Get Your Files Here !/4. Wrapper methods/10. Project 12 Backward feature elimination implementation.mp4 26.6 MB
  • ~Get Your Files Here !/3. Filter Method/2. Variance For Feature Selection.mp4 23.7 MB
  • ~Get Your Files Here !/3. Filter Method/8. Feature Selection using Anova-F Score.mp4 23.2 MB
  • ~Get Your Files Here !/3. Filter Method/12. Mutual Information to select feature from a dataset where target variable discre.mp4 17.7 MB
  • ~Get Your Files Here !/4. Wrapper methods/13. Exhaustive feature selection.mp4 16.6 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/6. Introduction to Regularization Methods for feature selection.mp4 12.4 MB
  • ~Get Your Files Here !/1. Introduction/1. Introduction.mp4 9.9 MB
  • ~Get Your Files Here !/2. Feature Selection Introduction/1. Feature Selection Introduction.mp4 9.2 MB
  • ~Get Your Files Here !/4. Wrapper methods/5. Forward Feature Selection in mlxtend.mp4 8.9 MB
  • ~Get Your Files Here !/4. Wrapper methods/11. Backward feature selection mlxtend.mp4 8.8 MB
  • ~Get Your Files Here !/3. Filter Method/1. Filter Method Introduction.mp4 7.2 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/2. Tree based methods.mp4 7.0 MB
  • ~Get Your Files Here !/4. Wrapper methods/8. Backward Feature Elimination sklearn.mp4 4.5 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/1. Introduction to Embedded Methods.mp4 3.6 MB
  • ~Get Your Files Here !/4. Wrapper methods/7. Backward Feature Elimination.mp4 3.1 MB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/9. Benefits of Embedded Methods.mp4 3.0 MB
  • ~Get Your Files Here !/4. Wrapper methods/1. Introduction to wrapper methods.mp4 1.6 MB
  • ~Get Your Files Here !/3. Filter Method/3. Project 1 Variance for Feature selection on data for classification.srt 19.6 kB
  • ~Get Your Files Here !/3. Filter Method/6. Feature selection using F-Score.srt 12.5 kB
  • ~Get Your Files Here !/3. Filter Method/4. Project 2 Variance for Feature selection on data for regression.srt 11.9 kB
  • ~Get Your Files Here !/3. Filter Method/10. Mutual information to select features in a datasets with continuous target.srt 11.6 kB
  • ~Get Your Files Here !/3. Filter Method/13. Project 6 Mutual information implementation on a dataset with discrete target.srt 10.1 kB
  • ~Get Your Files Here !/3. Filter Method/11. Project 5 To select features from a dataset using Mutual Information.srt 8.7 kB
  • ~Get Your Files Here !/3. Filter Method/14. Chi2 test method to select feature.srt 8.6 kB
  • ~Get Your Files Here !/3. Filter Method/9. Project 4 Feature selection using anova F-Score.srt 7.7 kB
  • ~Get Your Files Here !/3. Filter Method/7. Project 3 Feature selection using F Score.srt 7.5 kB
  • ~Get Your Files Here !/4. Wrapper methods/2. Forward Feature Selection.srt 7.2 kB
  • ~Get Your Files Here !/3. Filter Method/8. Feature Selection using Anova-F Score.srt 6.3 kB
  • ~Get Your Files Here !/4. Wrapper methods/12. Project 11 Backward feature selection implementation.srt 6.2 kB
  • ~Get Your Files Here !/3. Filter Method/2. Variance For Feature Selection.srt 5.9 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/8. Project 17 Implementation of Logistic Regression with Lasso Regularization.srt 5.8 kB
  • ~Get Your Files Here !/4. Wrapper methods/14. Project 12 Implementation of Exhaustive feature selection.srt 5.7 kB
  • ~Get Your Files Here !/4. Wrapper methods/9. Project 11 Backward feature elimination implementation sklearn.srt 5.5 kB
  • ~Get Your Files Here !/4. Wrapper methods/3. Project 8 Implementation of forward feature selection using sklearn.srt 5.3 kB
  • ~Get Your Files Here !/4. Wrapper methods/6. Project 10 Implementation of forward feature selection mlxtend.srt 5.2 kB
  • ~Get Your Files Here !/3. Filter Method/15. Project 7 Implementation of chi2.srt 5.1 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/3. Project 13 Implementation of Embedded Method using Decision Tree Classifier.srt 4.8 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/6. Introduction to Regularization Methods for feature selection.srt 4.2 kB
  • ~Get Your Files Here !/3. Filter Method/5. Project 2 Variance for Feature selection on data for regression part 2.srt 3.8 kB
  • ~Get Your Files Here !/4. Wrapper methods/13. Exhaustive feature selection.srt 3.8 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/4. Project 14 Implementation of Embedded Method using RandomForest Regressor.srt 3.6 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/5. Project 15 Implementation of Embedded Method using Extremely randomized trees.srt 3.6 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/7. Project 16 Implementation of Lasso Regularization.srt 3.5 kB
  • ~Get Your Files Here !/4. Wrapper methods/4. Project 9 Implementation of forward feature selection using sklearn.srt 3.3 kB
  • ~Get Your Files Here !/3. Filter Method/12. Mutual Information to select feature from a dataset where target variable discre.srt 3.1 kB
  • ~Get Your Files Here !/1. Introduction/1. Introduction.srt 2.9 kB
  • ~Get Your Files Here !/4. Wrapper methods/10. Project 12 Backward feature elimination implementation.srt 2.8 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/2. Tree based methods.srt 2.6 kB
  • ~Get Your Files Here !/3. Filter Method/1. Filter Method Introduction.srt 2.4 kB
  • ~Get Your Files Here !/2. Feature Selection Introduction/1. Feature Selection Introduction.srt 2.1 kB
  • ~Get Your Files Here !/4. Wrapper methods/11. Backward feature selection mlxtend.srt 1.8 kB
  • ~Get Your Files Here !/4. Wrapper methods/5. Forward Feature Selection in mlxtend.srt 1.8 kB
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/1. Introduction to Embedded Methods.srt 1.2 kB
  • ~Get Your Files Here !/4. Wrapper methods/7. Backward Feature Elimination.srt 855 Bytes
  • ~Get Your Files Here !/5. Embedded Methods for Feature Selection/9. Benefits of Embedded Methods.srt 832 Bytes
  • ~Get Your Files Here !/4. Wrapper methods/8. Backward Feature Elimination sklearn.srt 633 Bytes
  • ~Get Your Files Here !/4. Wrapper methods/1. Introduction to wrapper methods.srt 628 Bytes
  • ~Get Your Files Here !/Bonus Resources.txt 357 Bytes
  • Get Bonus Downloads Here.url 179 Bytes

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