[FreeTutorials.Eu] [UDEMY] Feature Selection for Machine Learning - [FTU]
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- 06 Wrapper methods/033 Step backward feature selection.mp4 32.1 MB
- 06 Wrapper methods/032 Step forward feature selection.mp4 29.6 MB
- 04 Filter methods Correlation/022 Correlation.mp4 24.4 MB
- 03 Filter Methods Basics/019 Duplicated features.mp4 20.7 MB
- 08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients.mp4 20.2 MB
- 06 Wrapper methods/034 Exhaustive search.mp4 18.7 MB
- 05 Filter methods Statistical measures/024 Statistical methods Intro.mp4 16.6 MB
- 05 Filter methods Statistical measures/027 Univariate approaches.mp4 16.4 MB
- 02 Feature Selection/012 Feature selection methods Overview.mp4 15.6 MB
- 06 Wrapper methods/031 Wrapper methods Intro.mp4 15.5 MB
- 03 Filter Methods Basics/018 Quasi-constant features.mp4 15.4 MB
- 03 Filter Methods Basics/017 Constant features.mp4 14.5 MB
- 05 Filter methods Statistical measures/025 Mutual information.mp4 14.0 MB
- 04 Filter methods Correlation/021 Correlation Intro.mp4 14.0 MB
- 07 Embedded methods Lasso regularisation/036 Lasso.mp4 13.9 MB
- 05 Filter methods Statistical measures/028 Univariate ROC-AUC.mp4 10.9 MB
- 02 Feature Selection/015 Embedded Methods.mp4 9.5 MB
- 09 Embedded methods Trees/043 Selecting Features by Tree importance Intro.mp4 9.3 MB
- 03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro.mp4 8.9 MB
- 08 Embedded methods Linear models/040 Coefficients change with penalty.mp4 8.5 MB
- 07 Embedded methods Lasso regularisation/035 Regularisation Intro.mp4 8.0 MB
- 02 Feature Selection/011 What is feature selection.mp4 7.8 MB
- 02 Feature Selection/014 Wrapper methods.mp4 7.3 MB
- 05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score.mp4 7.3 MB
- 01 Introduction/003 Course requirements.mp4 6.4 MB
- 01 Introduction/008 Feature-selection-presentations.zip 6.0 MB
- 08 Embedded methods Linear models/038 Regression Coefficients Intro.mp4 5.5 MB
- 08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients.mp4 5.1 MB
- 02 Feature Selection/013 Filter Methods.mp4 4.9 MB
- 01 Introduction/001 Introduction.mp4 4.6 MB
- 01 Introduction/002 Course Curriculum Overview.mp4 4.1 MB
- 01 Introduction/009 Feature-selection-notebooks.zip 915.1 KB
- Discuss.FreeTutorials.Us.html 165.7 KB
- FreeCoursesOnline.Me.html 108.3 KB
- FreeTutorials.Eu.html 102.2 KB
- 11 Hybrid feature selection methods/051 BONUS Hybrid method Recursive feature addition.html 51.1 KB
- 11 Hybrid feature selection methods/050 BONUS Hybrid method Recursive feature elimination.html 48.8 KB
- 11 Hybrid feature selection methods/049 BONUS Shuffling features.html 20.0 KB
- 05 Filter methods Statistical measures/030 BONUS select features by mean encoding KDD 2009.html 19.2 KB
- 04 Filter methods Correlation/022 Correlation-en.srt 18.7 KB
- 07 Embedded methods Lasso regularisation/037 Basic filter methods LASSO pipeline.html 16.1 KB
- 09 Embedded methods Trees/047 Feature selection with decision trees review.html 15.7 KB
- 08 Embedded methods Linear models/042 Feature selection with linear models review.html 15.5 KB
- 05 Filter methods Statistical measures/024 Statistical methods Intro-en.srt 15.5 KB
- 09 Embedded methods Trees/044 Select by model importance random forests embedded.html 15.1 KB
- 06 Wrapper methods/032 Step forward feature selection-en.srt 14.5 KB
- 06 Wrapper methods/033 Step backward feature selection-en.srt 14.5 KB
- 05 Filter methods Statistical measures/029 Basic methods Correlation univariate ROC-AUC pipeline.html 14.0 KB
- 03 Filter Methods Basics/017 Constant features-en.srt 12.8 KB
- 03 Filter Methods Basics/018 Quasi-constant features-en.srt 12.5 KB
- 05 Filter methods Statistical measures/027 Univariate approaches-en.srt 12.2 KB
- 04 Filter methods Correlation/023 Basic methods plus Correlation pipeline.html 11.1 KB
- 09 Embedded methods Trees/045 Select by model importance random forests recursively.html 11.1 KB
- 07 Embedded methods Lasso regularisation/036 Lasso-en.srt 10.4 KB
- 06 Wrapper methods/034 Exhaustive search-en.srt 10.3 KB
- 05 Filter methods Statistical measures/025 Mutual information-en.srt 10.0 KB
- 09 Embedded methods Trees/046 Select by model importance gradient boosted machines.html 9.6 KB
- 08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients-en.srt 9.5 KB
- 05 Filter methods Statistical measures/028 Univariate ROC-AUC-en.srt 8.8 KB
- 03 Filter Methods Basics/019 Duplicated features-en.srt 8.6 KB
- 06 Wrapper methods/031 Wrapper methods Intro-en.srt 8.4 KB
- 09 Embedded methods Trees/043 Selecting Features by Tree importance Intro-en.srt 8.2 KB
- 02 Feature Selection/011 What is feature selection-en.srt 7.4 KB
- 02 Feature Selection/012 Feature selection methods Overview-en.srt 7.3 KB
- 07 Embedded methods Lasso regularisation/035 Regularisation Intro-en.srt 6.8 KB
- 08 Embedded methods Linear models/040 Coefficients change with penalty-en.srt 6.7 KB
- 04 Filter methods Correlation/021 Correlation Intro-en.srt 6.6 KB
- 02 Feature Selection/014 Wrapper methods-en.srt 6.3 KB
- 05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score-en.srt 5.6 KB
- 01 Introduction/001 Introduction-en.srt 5.5 KB
- 08 Embedded methods Linear models/038 Regression Coefficients Intro-en.srt 5.2 KB
- 03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro-en.srt 4.9 KB
- 02 Feature Selection/015 Embedded Methods-en.srt 4.9 KB
- 01 Introduction/002 Course Curriculum Overview-en.srt 4.9 KB
- 03 Filter Methods Basics/020 Basic methods review.html 4.6 KB
- 01 Introduction/003 Course requirements-en.srt 4.4 KB
- 01 Introduction/006 Guide to setting up your computer.html 4.1 KB
- 08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients-en.srt 3.9 KB
- 02 Feature Selection/013 Filter Methods-en.srt 3.9 KB
- 01 Introduction/007 Installing XGBoost in windows.html 2.9 KB
- 10 Reading Resources/048 Additional reading resources.html 2.6 KB
- 01 Introduction/005 How to approach this course.html 2.4 KB
- 01 Introduction/010 FAQ Data Science and Python programming.html 1.8 KB
- 01 Introduction/004 Additional Requirements Nice to have.html 1.5 KB
- 12 Final section Next steps/052 Bonus Lecture Discounts on my other courses.html 1.3 KB
- 01 Introduction/008 Presentations covered in this course.html 994 bytes
- 01 Introduction/009 Jupyter notebooks covered in this course.html 994 bytes
- [TGx]Downloaded from torrentgalaxy.org.txt 524 bytes
- Torrent Downloaded From GloDls.to.txt 84 bytes
- 07 Embedded methods Lasso regularisation/035 Machine-Learning-Explained-Regularization.txt 71 bytes
- 07 Embedded methods Lasso regularisation/035 Least-angle-and-1-penalized-regression-A-review-.txt 68 bytes
- Presented By SaM.txt 33 bytes
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