Udemy - Feature Engineering for Machine Learning by Soledad Galli

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3 GB
  • Uploaded Byfreecoursewb
  • Downloads38
  • Last checkedApr. 24th '22
  • Date uploadedApr. 22nd '22
  • Seeders 9
  • Leechers8
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Infohash : 6E09C80F8E5673A04A96C7259BA2E2563986145E

Feature Engineering for Machine Learning by Soledad Galli



https://DevCourseWeb.com

Updated 03/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 138 lectures (10h 28m) | Size: 3.1 GB

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more

What you'll learn
Learn multiple techniques for missing data imputation.
Transform categorical variables into numbers while capturing meaningful information.
Learn how to deal with infrequent, rare, and unseen categories.
Learn how to work with skewed variables.
Convert numerical variables into discrete ones.
Remove outliers from your variables.
Extract useful features from dates and time variables.
Learn techniques used in organizations worldwide and in data competitions.
Increase your repertoire of techniques to preprocess data and build more powerful machine learning models.

Requirements
A Python installation.
Jupyter notebook installation.
Python coding skills.
Some experience with Numpy and Pandas.
Familiarity with machine learning algorithms.
Familiarity with Scikit-Learn.

Files:

[ DevCourseWeb.com ] Udemy - Feature Engineering for Machine Learning by Soledad Galli
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 - Introduction
    • 001 Course curriculum overview.mp4 (49.6 MB)
    • 001 Course curriculum overview_en.srt (7.0 KB)
    • 002 Course requirements.mp4 (20.5 MB)
    • 002 Course requirements_en.srt (3.5 KB)
    • 003 How to approach this course.html (1.7 KB)
    • 004 Setting up your computer.html (3.2 KB)
    • 005 Course material.mp4 (5.8 MB)
    • 005 Course material_en.srt (2.3 KB)
    • 006 Download Jupyter notebooks.html (1.0 KB)
    • 007 Download datasets.html (3.5 KB)
    • 008 Download presentations.html (0.3 KB)
    • 009 Moving forward.mp4 (3.9 MB)
    • 009 Moving forward_en.srt (2.5 KB)
    • 010 FAQ Data science, Python, datasets, presentations and more.html (2.0 KB)
    • loan.csv (1.0 MB)
    • sample_s2.csv (9.9 MB)
    02 - Variable Types
    • 001 Variables Intro.mp4 (5.4 MB)
    • 001 Variables Intro_en.srt (3.5 KB)
    • 002 Numerical variables.mp4 (14.8 MB)
    • 002 Numerical variables_en.srt (7.0 KB)
    • 003 Categorical variables.mp4 (7.6 MB)
    • 003 Categorical variables_en.srt (4.6 KB)
    • 004 Date and time variables.mp4 (4.2 MB)
    • 004 Date and time variables_en.srt (2.5 KB)
    • 005 Mixed variables.mp4 (4.6 MB)
    • 005 Mixed variables_en.srt (2.8 KB)
    • 005 sample-s2.csv (9.9 MB)
    03 - Variable Characteristics
    • 001 Variable characteristics.mp4 (7.2 MB)
    • 001 Variable characteristics_en.srt (3.5 KB)
    • 002 Missing data.mp4 (21.5 MB)
    • 002 Missing data_en.srt (9.0 KB)
    • 003 Cardinality - categorical variables.mp4 (22.5 MB)
    • 003 Cardinality - categorical variables_en.srt (6.4 KB)
    • 004 Rare labels - categorical variables.mp4 (14.5 MB)
    • 004 Rare labels - categorical variables_en.srt (6.2 KB)
    • 005 Linear models assumptions.mp4 (41.5 MB)
    • 005 Linear models assumptions_en.srt (10.9 KB)
    • 006 Linear model assumptions - additional reading resources (optional).html (1.5 KB)
    • 007 Variable distribution.mp4 (14.9 MB)
    • 007 Variable distribution_en.srt (6.5 KB)
    • 008 Outliers.mp4 (18.6 MB)
    • 008 Outliers_en.srt (10.7 KB)
    • 009 Variable magnitude.mp4 (7.4 MB)
    • 009 Variable magnitude_en.srt (4.0 KB)
    • 010 ML-Comparison.pdf (297.6 KB)
    • 010 Variable characteristics and machine learning models.html (0.4 KB)
    • 011 Additional reading resources.html (4.5 KB)
    04 - Missing Data Imputation
    • 001 Introduction to missing data imputation.mp4 (17.9 MB)
    • 001 Introduction to missing data imputation_en.srt (5.2 KB)
    • 002 Complete Case Analysis.mp4 (39.2 MB)
    • 002 Complete Case Analysis_en.srt (8.6 KB)
    • 003 Mean or median imputation.mp4 (25.9 MB)
    • 003 Mean or median imputation_en.srt (10.3 KB)
    • 004 Arbitrary value imputation.mp4 (30.7 MB)
    • 004 Arbitrary value imputation_en.srt (8.8 KB)
    • 005 End of distribution imputation.mp4 (18.2 MB)
    • 005 End of distribution imputation_en.srt (6.1 KB)
    • 006 Frequent category imputation.mp4 (38.1 MB)
    • 006 Frequent category imputation_en.srt (8.6 KB)
    • 007 Missing category imputation.mp4 (23.4 MB)
    • 007 Missing category imputation_en.srt (5.0 KB)
    • 008 Random sample imputation.mp4 (87.6 MB)
    • 008 Random sample imputation_en.srt (18.2 KB)
    • 009 Adding a missing indicator.mp4 (14.7 MB)
    • 009 Adding a missing indicator_en.srt (6.9 KB)
    • 010 Imputation with Scikit-learn.mp4 (20.8 MB)
    • 010 Imputation with Scikit-learn_en.srt (5.1 KB)
    • 011 Mean or median imputation with Scikit-learn.mp4 (37.9 MB)
    • 011 Mean or median imputation with Scikit-learn_en.srt (6.5 KB)
    • 012 Arbitrary value imputation with Scikit-learn.mp4 (36.4 MB)
    • 012 Arbitrary value imputation with Scikit-learn_en.srt (6.4 KB)
    • 013 Frequent category imputation with Scikit-learn.mp4 (35.3 MB)
    • 013 Frequent category imputation with Scikit-learn_en.srt (6.7 KB)
    • 014 Missing category imputation with Scikit-learn.mp4 (20.0 MB)
    • 014 Missing category imputation with Scikit-learn_en.srt (3.6 KB)
    • 015 Adding a missing indicator with Scikit-learn.mp4 (23.3 MB)
    • 015 Adding a missing indicator with Scikit-learn_en.srt (4.6 KB)
    • 016 Automatic determination of imputation method with Sklearn.mp4 (65.4 MB)
    • 016 Automatic determination of imputation method with Sklearn_en.srt (9.2 KB)
    • 017 Introduction to Feature-engine.mp4 (26.9 MB)
    • 017 Introduction to Feature-engine_en.srt (8.3 KB)
    • 018 Mean or median imputation with Feature-engine.mp4 (31.7 MB)
    • 018 Mean or median imputation with Feature-engine_en.srt (5.5 KB)
    • 019 Arbitrary value imputation with Feature-engine.mp4 (25.1 MB)
    • 019 Arbitrary value imputation with Feature-engine_en.srt (3.8 KB)
    • 020 End of distribution imputation with Feature-engine.mp4 (26.0 MB)
    • 020 End of distribution imputation with Feature-engine_en.srt (5.8 KB)
    • 021 Frequent category imputation with Feature-engine.mp4 (5.3 MB)
    • 021 Frequent category imputation with Feature-engine_en.srt (2.0 KB)
    • 022 Missing category imputation with Feature-engine.mp4 (19.8 MB)
    • 022 Missing category imputation with Feature-engine_en.srt (3.8 KB)
    • 023 Random sample imputation with Feature-engine.mp4 (16.9 MB)
    • 023 Random sample imputation with Feature-engine_en.srt (2.9 KB)
    • 024 Adding a missing indicator with Feature-engine.mp4 (28.0 MB)
    • 024 Adding a missing indicator with Feature-engine_en.srt (4.9 KB)
    • 025 CCA with Feature-engine.mp4 (37.3 MB)
    • 025 CCA with Feature-engine_en.srt (8.5 KB)
    • 026 NA-methods-Comparison.pdf (273.8 KB)
    • 026 Overview of missing value imputation methods.html (0.3 KB)
    • 027 Conclusion when to use each missing data imputation method.html (2.7 KB)
    05 - Multivariate Missing Data Imputation
    • 001 Multivariate imputation.mp4 (7.5 MB)

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