[FreeTutorials.Eu] [UDEMY] Feature Selection for Machine Learning - [FTU]
大小
397.11 MB
文件数
92
Info Hash:
722B3338485097FF62F7925C2F6484415B2837C9
收录时间
2025-12-23 08:26:57
更新时间
2025-12-23 13:20:28
文件列表 (92)
01 Introduction/001 Introduction-en.srt
5.48 KB
01 Introduction/001 Introduction.mp4
4.62 MB
01 Introduction/002 Course Curriculum Overview-en.srt
4.91 KB
01 Introduction/002 Course Curriculum Overview.mp4
4.05 MB
01 Introduction/003 Course requirements-en.srt
4.43 KB
01 Introduction/003 Course requirements.mp4
6.42 MB
01 Introduction/004 Additional Requirements Nice to have.html
1.51 KB
01 Introduction/005 How to approach this course.html
2.38 KB
01 Introduction/006 Guide to setting up your computer.html
4.11 KB
01 Introduction/007 Installing XGBoost in windows.html
2.93 KB
01 Introduction/008 Feature-selection-presentations.zip
5.97 MB
01 Introduction/008 Presentations covered in this course.html
994 B
01 Introduction/009 Feature-selection-notebooks.zip
915.13 KB
01 Introduction/009 Jupyter notebooks covered in this course.html
994 B
01 Introduction/010 FAQ Data Science and Python programming.html
1.81 KB
02 Feature Selection/011 What is feature selection-en.srt
7.42 KB
02 Feature Selection/011 What is feature selection.mp4
7.82 MB
02 Feature Selection/012 Feature selection methods Overview-en.srt
7.3 KB
02 Feature Selection/012 Feature selection methods Overview.mp4
15.55 MB
02 Feature Selection/013 Filter Methods-en.srt
3.91 KB
02 Feature Selection/013 Filter Methods.mp4
4.87 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
4.93 KB
02 Feature Selection/015 Embedded Methods.mp4
9.53 MB
03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro-en.srt
4.95 KB
03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro.mp4
8.87 MB
03 Filter Methods Basics/017 Constant features-en.srt
12.76 KB
03 Filter Methods Basics/017 Constant features.mp4
14.5 MB
03 Filter Methods Basics/018 Quasi-constant features-en.srt
12.49 KB
03 Filter Methods Basics/018 Quasi-constant features.mp4
15.38 MB
03 Filter Methods Basics/019 Duplicated features-en.srt
8.64 KB
03 Filter Methods Basics/019 Duplicated features.mp4
20.7 MB
03 Filter Methods Basics/020 Basic methods review.html
4.61 KB
04 Filter methods Correlation/021 Correlation Intro-en.srt
6.63 KB
04 Filter methods Correlation/021 Correlation Intro.mp4
13.96 MB
04 Filter methods Correlation/022 Correlation-en.srt
18.68 KB
04 Filter methods Correlation/022 Correlation.mp4
24.38 MB
04 Filter methods Correlation/023 Basic methods plus Correlation pipeline.html
11.12 KB
05 Filter methods Statistical measures/024 Statistical methods Intro-en.srt
15.46 KB
05 Filter methods Statistical measures/024 Statistical methods Intro.mp4
16.57 MB
05 Filter methods Statistical measures/025 Mutual information-en.srt
9.97 KB
05 Filter methods Statistical measures/025 Mutual information.mp4
14.03 MB
05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score-en.srt
5.57 KB
05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score.mp4
7.27 MB
05 Filter methods Statistical measures/027 Univariate approaches-en.srt
12.21 KB
05 Filter methods Statistical measures/027 Univariate approaches.mp4
16.43 MB
05 Filter methods Statistical measures/028 Univariate ROC-AUC-en.srt
8.78 KB
05 Filter methods Statistical measures/028 Univariate ROC-AUC.mp4
10.87 MB
05 Filter methods Statistical measures/029 Basic methods Correlation univariate ROC-AUC pipeline.html
14.04 KB
05 Filter methods Statistical measures/030 BONUS select features by mean encoding KDD 2009.html
19.21 KB
06 Wrapper methods/031 Wrapper methods Intro-en.srt
8.38 KB
06 Wrapper methods/031 Wrapper methods Intro.mp4
15.55 MB
06 Wrapper methods/032 Step forward feature selection-en.srt
14.48 KB
06 Wrapper methods/032 Step forward feature selection.mp4
29.59 MB
06 Wrapper methods/033 Step backward feature selection-en.srt
14.46 KB
06 Wrapper methods/033 Step backward feature selection.mp4
32.07 MB
06 Wrapper methods/034 Exhaustive search-en.srt
10.26 KB
06 Wrapper methods/034 Exhaustive search.mp4
18.68 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
6.78 KB
07 Embedded methods Lasso regularisation/035 Regularisation Intro.mp4
7.95 MB
07 Embedded methods Lasso regularisation/036 Lasso-en.srt
10.39 KB
07 Embedded methods Lasso regularisation/036 Lasso.mp4
13.93 MB
07 Embedded methods Lasso regularisation/037 Basic filter methods LASSO pipeline.html
16.14 KB
08 Embedded methods Linear models/038 Regression Coefficients Intro-en.srt
5.22 KB
08 Embedded methods Linear models/038 Regression Coefficients Intro.mp4
5.48 MB
08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients-en.srt
9.54 KB
08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients.mp4
20.16 MB
08 Embedded methods Linear models/040 Coefficients change with penalty-en.srt
6.74 KB
08 Embedded methods Linear models/040 Coefficients change with penalty.mp4
8.49 MB
08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients-en.srt
3.94 KB
08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients.mp4
5.08 MB
08 Embedded methods Linear models/042 Feature selection with linear models review.html
15.52 KB
09 Embedded methods Trees/043 Selecting Features by Tree importance Intro-en.srt
8.22 KB
09 Embedded methods Trees/043 Selecting Features by Tree importance Intro.mp4
9.28 MB
09 Embedded methods Trees/044 Select by model importance random forests embedded.html
15.11 KB
09 Embedded methods Trees/045 Select by model importance random forests recursively.html
11.08 KB
09 Embedded methods Trees/046 Select by model importance gradient boosted machines.html
9.64 KB
09 Embedded methods Trees/047 Feature selection with decision trees review.html
15.75 KB
10 Reading Resources/048 Additional reading resources.html
2.57 KB
11 Hybrid feature selection methods/049 BONUS Shuffling features.html
19.98 KB
11 Hybrid feature selection methods/050 BONUS Hybrid method Recursive feature elimination.html
48.79 KB
11 Hybrid feature selection methods/051 BONUS Hybrid method Recursive feature addition.html
51.08 KB
12 Final section Next steps/052 Bonus Lecture Discounts on my other courses.html
1.34 KB
Discuss.FreeTutorials.Us.html
165.68 KB
FreeCoursesOnline.Me.html
108.3 KB
FreeTutorials.Eu.html
102.23 KB
Presented By SaM.txt
33 B
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524 B
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84 B