CoursePig com Udemy Complete Bootcamp 2021 Feature selection using Python

CoursePig com Udemy Complete Bootcamp 2021 Feature selection using Python

Name	[ CoursePig.com ] Udemy - Complete Bootcamp 2021 - Feature selection using Python	DOWNLOAD Copy Link
Total Size	1.5 GB
Total Files	82
Hash	4AD40D8152D85BBE41F2FFEAE39FC6B6CB9499E4

/
Get Bonus Downloads Here.url	0.2 KB
/1. Introduction/
1. Introduction.mp4	9.9 MB
1. Introduction.srt	2.9 KB
/.../2. Feature Selection Introduction/
1. Feature Selection Introduction.mp4	9.2 MB
1. Feature Selection Introduction.srt	2.1 KB
/3. Filter Method/
1. Filter Method Introduction.mp4	7.2 MB
1. Filter Method Introduction.srt	2.4 KB
10. Mutual information to select features in a datasets with continuous target.mp4	43.3 MB
10. Mutual information to select features in a datasets with continuous target.srt	11.6 KB
11. Project 5 To select features from a dataset using Mutual Information.mp4	71.8 MB
11. Project 5 To select features from a dataset using Mutual Information.srt	8.7 KB
12. Mutual Information to select feature from a dataset where target variable discre.mp4	17.7 MB
12. Mutual Information to select feature from a dataset where target variable discre.srt	3.1 KB
13. Project 6 Mutual information implementation on a dataset with discrete target.mp4	83.1 MB
13. Project 6 Mutual information implementation on a dataset with discrete target.srt	10.1 KB
14. Chi2 test method to select feature.mp4	28.2 MB
14. Chi2 test method to select feature.srt	8.6 KB
15. Project 7 Implementation of chi2.mp4	45.7 MB
15. Project 7 Implementation of chi2.srt	5.1 KB
2. Variance For Feature Selection.mp4	23.7 MB
2. Variance For Feature Selection.srt	5.9 KB
3. Project 1 Variance for Feature selection on data for classification.mp4	191.6 MB
3. Project 1 Variance for Feature selection on data for classification.srt	19.6 KB
4. Project 2 Variance for Feature selection on data for regression.mp4	123.3 MB
4. Project 2 Variance for Feature selection on data for regression.srt	11.9 KB
5. Project 2 Variance for Feature selection on data for regression part 2.mp4	26.9 MB
5. Project 2 Variance for Feature selection on data for regression part 2.srt	3.8 KB
6. Feature selection using F-Score.mp4	42.4 MB
6. Feature selection using F-Score.srt	12.5 KB
7. Project 3 Feature selection using F Score.mp4	71.3 MB
7. Project 3 Feature selection using F Score.srt	7.5 KB
8. Feature Selection using Anova-F Score.mp4	23.2 MB
8. Feature Selection using Anova-F Score.srt	6.3 KB
9. Project 4 Feature selection using anova F-Score.mp4	81.2 MB
9. Project 4 Feature selection using anova F-Score.srt	7.7 KB
/4. Wrapper methods/
1. Introduction to wrapper methods.mp4	1.6 MB
1. Introduction to wrapper methods.srt	0.6 KB
10. Project 12 Backward feature elimination implementation.mp4	26.6 MB
10. Project 12 Backward feature elimination implementation.srt	2.8 KB
11. Backward feature selection mlxtend.mp4	8.8 MB
11. Backward feature selection mlxtend.srt	1.8 KB
12. Project 11 Backward feature selection implementation.mp4	62.0 MB
12. Project 11 Backward feature selection implementation.srt	6.2 KB
13. Exhaustive feature selection.mp4	16.6 MB
13. Exhaustive feature selection.srt	3.8 KB
14. Project 12 Implementation of Exhaustive feature selection.mp4	60.1 MB
14. Project 12 Implementation of Exhaustive feature selection.srt	5.7 KB
2. Forward Feature Selection.mp4	37.2 MB
2. Forward Feature Selection.srt	7.2 KB
3. Project 8 Implementation of forward feature selection using sklearn.mp4	61.3 MB
3. Project 8 Implementation of forward feature selection using sklearn.srt	5.3 KB
4. Project 9 Implementation of forward feature selection using sklearn.mp4	30.8 MB
4. Project 9 Implementation of forward feature selection using sklearn.srt	3.3 KB
5. Forward Feature Selection in mlxtend.mp4	8.9 MB
5. Forward Feature Selection in mlxtend.srt	1.8 KB
6. Project 10 Implementation of forward feature selection mlxtend.mp4	54.1 MB
6. Project 10 Implementation of forward feature selection mlxtend.srt	5.2 KB
7. Backward Feature Elimination.mp4	3.1 MB
7. Backward Feature Elimination.srt	0.9 KB
8. Backward Feature Elimination sklearn.mp4	4.5 MB
8. Backward Feature Elimination sklearn.srt	0.6 KB
9. Project 11 Backward feature elimination implementation sklearn.mp4	48.0 MB
9. Project 11 Backward feature elimination implementation sklearn.srt	5.5 KB
/.../5. Embedded Methods for Feature Selection/
1. Introduction to Embedded Methods.mp4	3.6 MB
1. Introduction to Embedded Methods.srt	1.2 KB
2. Tree based methods.mp4	7.0 MB
2. Tree based methods.srt	2.6 KB
3. Project 13 Implementation of Embedded Method using Decision Tree Classifier.mp4	42.3 MB
3. Project 13 Implementation of Embedded Method using Decision Tree Classifier.srt	4.8 KB
4. Project 14 Implementation of Embedded Method using RandomForest Regressor.mp4	35.2 MB
4. Project 14 Implementation of Embedded Method using RandomForest Regressor.srt	3.6 KB
5. Project 15 Implementation of Embedded Method using Extremely randomized trees.mp4	36.2 MB
5. Project 15 Implementation of Embedded Method using Extremely randomized trees.srt	3.6 KB
6. Introduction to Regularization Methods for feature selection.mp4	12.4 MB
6. Introduction to Regularization Methods for feature selection.srt	4.2 KB
7. Project 16 Implementation of Lasso Regularization.mp4	27.3 MB
7. Project 16 Implementation of Lasso Regularization.srt	3.5 KB
8. Project 17 Implementation of Logistic Regression with Lasso Regularization.mp4	55.5 MB
8. Project 17 Implementation of Logistic Regression with Lasso Regularization.srt	5.8 KB
9. Benefits of Embedded Methods.mp4	3.0 MB
9. Benefits of Embedded Methods.srt	0.8 KB
/~Get Your Files Here !/
Bonus Resources.txt	0.4 KB
Total files 82