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[FreeTutorials Eu] [UDEMY] Feature Selection for Machine Learning - [FTU]
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Torrent Files List
01 Introduction (Size: 397.11 MB) (Files: 92)
01 Introduction
001 Introduction-en.srt
001 Introduction.mp4
002 Course Curriculum Overview-en.srt
002 Course Curriculum Overview.mp4
003 Course requirements-en.srt
003 Course requirements.mp4
004 Additional Requirements Nice to have.html
005 How to approach this course.html
006 Guide to setting up your computer.html
007 Installing XGBoost in windows.html
008 Feature-selection-presentations.zip
008 Presentations covered in this course.html
009 Feature-selection-notebooks.zip
009 Jupyter notebooks covered in this course.html
010 FAQ Data Science and Python programming.html
02 Feature Selection
011 What is feature selection-en.srt
011 What is feature selection.mp4
012 Feature selection methods Overview-en.srt
012 Feature selection methods Overview.mp4
013 Filter Methods-en.srt
013 Filter Methods.mp4
014 Wrapper methods-en.srt
014 Wrapper methods.mp4
015 Embedded Methods-en.srt
015 Embedded Methods.mp4
03 Filter Methods Basics
016 Constant quasi constant and duplicated features Intro-en.srt
016 Constant quasi constant and duplicated features Intro.mp4
017 Constant features-en.srt
017 Constant features.mp4
018 Quasi-constant features-en.srt
018 Quasi-constant features.mp4
019 Duplicated features-en.srt
019 Duplicated features.mp4
020 Basic methods review.html
04 Filter methods Correlation
021 Correlation Intro-en.srt
021 Correlation Intro.mp4
022 Correlation-en.srt
022 Correlation.mp4
023 Basic methods plus Correlation pipeline.html
05 Filter methods Statistical measures
024 Statistical methods Intro-en.srt
024 Statistical methods Intro.mp4
025 Mutual information-en.srt
025 Mutual information.mp4
026 Chi-square for categorical variables Fisher score-en.srt
026 Chi-square for categorical variables Fisher score.mp4
027 Univariate approaches-en.srt
027 Univariate approaches.mp4
028 Univariate ROC-AUC-en.srt
028 Univariate ROC-AUC.mp4
029 Basic methods Correlation univariate ROC-AUC pipeline.html
030 BONUS select features by mean encoding KDD 2009.html
06 Wrapper methods
031 Wrapper methods Intro-en.srt
031 Wrapper methods Intro.mp4
032 Step forward feature selection-en.srt
032 Step forward feature selection.mp4
033 Step backward feature selection-en.srt
033 Step backward feature selection.mp4
034 Exhaustive search-en.srt
034 Exhaustive search.mp4
07 Embedded methods Lasso regularisation
035 Least-angle-and-1-penalized-regression-A-review-.txt
035 Machine-Learning-Explained-Regularization.txt
035 Regularisation Intro-en.srt
035 Regularisation Intro.mp4
036 Lasso-en.srt
036 Lasso.mp4
037 Basic filter methods LASSO pipeline.html
08 Embedded methods Linear models
038 Regression Coefficients Intro-en.srt
038 Regression Coefficients Intro.mp4
039 Selection by Logistic Regression Coefficients-en.srt
039 Selection by Logistic Regression Coefficients.mp4
040 Coefficients change with penalty-en.srt
040 Coefficients change with penalty.mp4
041 Selection by Linear Regression Coefficients-en.srt
041 Selection by Linear Regression Coefficients.mp4
042 Feature selection with linear models review.html
09 Embedded methods Trees
043 Selecting Features by Tree importance Intro-en.srt
043 Selecting Features by Tree importance Intro.mp4
044 Select by model importance random forests embedded.html
045 Select by model importance random forests recursively.html
046 Select by model importance gradient boosted machines.html
047 Feature selection with decision trees review.html
10 Reading Resources
048 Additional reading resources.html
11 Hybrid feature selection methods
049 BONUS Shuffling features.html
050 BONUS Hybrid method Recursive feature elimination.html
051 BONUS Hybrid method Recursive feature addition.html
12 Final section Next steps
052 Bonus Lecture Discounts on my other courses.html
Discuss.FreeTutorials.Us.html
FreeCoursesOnline.Me.html
FreeTutorials.Eu.html
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