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001 Introduction-en.srt
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001 Introduction.mp4
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002 Course Curriculum Overview-en.srt
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002 Course Curriculum Overview.mp4
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003 Course requirements-en.srt
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003 Course requirements.mp4
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004 Additional Requirements Nice to have.html
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005 How to approach this course.html
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006 Guide to setting up your computer.html
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007 Installing XGBoost in windows.html
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008 Feature-selection-presentations.zip
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008 Presentations covered in this course.html
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009 Feature-selection-notebooks.zip
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009 Jupyter notebooks covered in this course.html
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010 FAQ Data Science and Python programming.html
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011 What is feature selection-en.srt
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011 What is feature selection.mp4
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012 Feature selection methods Overview-en.srt
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012 Feature selection methods Overview.mp4
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013 Filter Methods-en.srt
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013 Filter Methods.mp4
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014 Wrapper methods-en.srt
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014 Wrapper methods.mp4
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015 Embedded Methods-en.srt
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015 Embedded Methods.mp4
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016 Constant quasi constant and duplicated features Intro-en.srt
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016 Constant quasi constant and duplicated features Intro.mp4
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017 Constant features-en.srt
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017 Constant features.mp4
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018 Quasi-constant features-en.srt
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018 Quasi-constant features.mp4
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019 Duplicated features-en.srt
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019 Duplicated features.mp4
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020 Basic methods review.html
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021 Correlation Intro-en.srt
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021 Correlation Intro.mp4
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022 Correlation-en.srt
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022 Correlation.mp4
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023 Basic methods plus Correlation pipeline.html
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024 Statistical methods Intro-en.srt
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024 Statistical methods Intro.mp4
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025 Mutual information-en.srt
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025 Mutual information.mp4
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026 Chi-square for categorical variables Fisher score-en.srt
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026 Chi-square for categorical variables Fisher score.mp4
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027 Univariate approaches-en.srt
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027 Univariate approaches.mp4
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028 Univariate ROC-AUC-en.srt
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028 Univariate ROC-AUC.mp4
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029 Basic methods Correlation univariate ROC-AUC pipeline.html
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030 BONUS select features by mean encoding KDD 2009.html
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031 Wrapper methods Intro-en.srt
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031 Wrapper methods Intro.mp4
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032 Step forward feature selection-en.srt
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032 Step forward feature selection.mp4
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033 Step backward feature selection-en.srt
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033 Step backward feature selection.mp4
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034 Exhaustive search-en.srt
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034 Exhaustive search.mp4
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035 Least-angle-and-1-penalized-regression-A-review-.txt
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035 Machine-Learning-Explained-Regularization.txt
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035 Regularisation Intro-en.srt
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035 Regularisation Intro.mp4
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036 Lasso-en.srt
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036 Lasso.mp4
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037 Basic filter methods LASSO pipeline.html
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038 Regression Coefficients Intro-en.srt
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038 Regression Coefficients Intro.mp4
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039 Selection by Logistic Regression Coefficients-en.srt
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039 Selection by Logistic Regression Coefficients.mp4
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040 Coefficients change with penalty-en.srt
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040 Coefficients change with penalty.mp4
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041 Selection by Linear Regression Coefficients-en.srt
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041 Selection by Linear Regression Coefficients.mp4
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042 Feature selection with linear models review.html
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043 Selecting Features by Tree importance Intro-en.srt
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043 Selecting Features by Tree importance Intro.mp4
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044 Select by model importance random forests embedded.html
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045 Select by model importance random forests recursively.html
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046 Select by model importance gradient boosted machines.html
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047 Feature selection with decision trees review.html
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048 Additional reading resources.html
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049 BONUS Shuffling features.html
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050 BONUS Hybrid method Recursive feature elimination.html
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051 BONUS Hybrid method Recursive feature addition.html
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052 Bonus Lecture Discounts on my other courses.html
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Discuss.FreeTutorials.Us.html
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FreeCoursesOnline.Me.html
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FreeTutorials.Eu.html
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Presented By SaM.txt
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0 B
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Torrent Downloaded From GloDls.to.txt
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[TGx]Downloaded from torrentgalaxy.org.txt
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512 B
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![[UDEMY] Feature Selection for Machine Learning - [FTU]](/static/img/no-torrent-image.png "[UDEMY] Feature Selection for Machine Learning - [FTU]")
