| 文件 | 大小 |
|---|
| 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 | 994.00 B |
| 01 Introduction/009 Feature-selection-notebooks.zip | 915.13 kB |
| 01 Introduction/009 Jupyter notebooks covered in this course.html | 994.00 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 | 7.30 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 | 6.30 kB |
| 02 Feature Selection/014 Wrapper methods.mp4 | 7.30 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 | 14.50 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 | 20.70 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 | 68.00 B |
| 07 Embedded methods Lasso regularisation/035 Machine-Learning-Explained-Regularization.txt | 71.00 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 | 108.30 kB |
| FreeTutorials.Eu.html | 102.23 kB |
| Presented By SaM.txt | 33.00 B |
| Torrent Downloaded From GloDls.to.txt | 84.00 B |
| [TGx]Downloaded from torrentgalaxy.org.txt | 524.00 B |
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