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[FreeTutorials.Eu] [UDEMY] Feature Selection for Machine Learning - [FTU]

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