| 001 Categorical encoding Introduction.mp4 | 34 MB | ||
| 001 Categorical encoding Introduction_en.srt | 8.3 KB | ||
| 001 Course curriculum overview.mp4 | 49.6 MB | ||
| 001 Course curriculum overview_en.srt | 7 KB | ||
| 001 Discretisation Introduction.mp4 | 15.4 MB | ||
| 001 Discretisation Introduction_en.srt | 3.5 KB | ||
| 001 Engineering datetime variables.mp4 | 13.4 MB | ||
| 001 Engineering datetime variables_en.srt | 5.6 KB | ||
| 001 Engineering mixed variables.mp4 | 11.7 MB | ||
| 001 Engineering mixed variables_en.srt | 4 KB | ||
| 001 Feature scaling Introduction.mp4 | 9.1 MB | ||
| 001 Feature scaling Introduction_en.srt | 4.7 KB | ||
| 001 Introduction to missing data imputation.mp4 | 17.9 MB | ||
| 001 Introduction to missing data imputation_en.srt | 5.2 KB | ||
| 001 Multivariate imputation.mp4 | 7.5 MB | ||
| 001 Multivariate imputation_en.srt | 3.9 KB | ||
| 001 Outlier Engineering Intro.mp4 | 32.2 MB | ||
| 001 Outlier Engineering Intro_en.srt | 8 KB | ||
| 001 Putting it all together.mp4 | 33 MB | ||
| 001 Putting it all together_en.srt | 8.9 KB | ||
| 001 Survey.html | 921.6 B | ||
| 001 Variable Transformation Introduction.mp4 | 9.3 MB | ||
| 001 Variable Transformation Introduction_en.srt | 5.6 KB | ||
| 001 Variable characteristics.mp4 | 7.2 MB | ||
| 001 Variable characteristics_en.srt | 3.5 KB | ||
| 001 Variables Intro.mp4 | 5.4 MB | ||
| 001 Variables Intro_en.srt | 3.5 KB | ||
| 002 Complete Case Analysis.mp4 | 39.2 MB | ||
| 002 Complete Case Analysis_en.srt | 8.6 KB | ||
| 002 Congratulations.html | 614.4 B | ||
| 002 Course requirements.mp4 | 20.5 MB | ||
| 002 Course requirements_en.srt | 3.5 KB | ||
| 002 Engineering dates Demo.mp4 | 39.6 MB | ||
| 002 Engineering dates Demo_en.srt | 9.5 KB | ||
| 002 Engineering mixed variables Demo.mp4 | 39.5 MB | ||
| 002 Engineering mixed variables Demo_en.srt | 7.7 KB | ||
| 002 Equal-width discretisation.mp4 | 9.1 MB | ||
| 002 Equal-width discretisation_en.srt | 4.5 KB | ||
| 002 Feature Engineering Pipeline.mp4 | 22 MB | ||
| 002 Feature Engineering Pipeline_en.srt | 10.7 KB | ||
| 002 KNN imputation.mp4 | 9.5 MB | ||
| 002 KNN imputation_en.srt | 4.9 KB | ||
| 002 Missing data.mp4 | 21.5 MB | ||
| 002 Missing data_en.srt | 9 KB | ||
| 002 Numerical variables.mp4 | 14.8 MB | ||
| 002 Numerical variables_en.srt | 7 KB | ||
| 002 One hot encoding.mp4 | 13.7 MB | ||
| 002 One hot encoding_en.srt | 7.2 KB | ||
| 002 Outlier trimming.mp4 | 37.5 MB | ||
| 002 Outlier trimming_en.srt | 8.5 KB | ||
| 002 Standardisation.mp4 | 11.6 MB | ||
| 002 Standardisation_en.srt | 6.7 KB | ||
| 002 Variable Transformation with Numpy and SciPy.mp4 | 42.5 MB | ||
| 002 Variable Transformation with Numpy and SciPy_en.srt | 8.7 KB | ||
| 003 Bonus lecture.html | 614.4 B | ||
| 003 Cardinality - categorical variables.mp4 | 22.5 MB | ||
| 003 Cardinality - categorical variables_en.srt | 6.4 KB | ||
| 003 Categorical variables.mp4 | 7.6 MB | ||
| 003 Categorical variables_en.srt | 4.6 KB | ||
| 003 Classification pipeline.mp4 | 76.6 MB | ||
| 003 Classification pipeline_en.srt | 16.6 KB | ||
| 003 Engineering time variables and different timezones.mp4 | 23.9 MB | ||
| 003 Engineering time variables and different timezones_en.srt | 5.7 KB | ||
| 003 How to approach this course.html | 1.7 KB | ||
| 003 Important Feature-engine v 1.0.0.html | 716.8 B | ||
| 003 Important Feature-engine version 1.0.0.html | 1 KB | ||
| 003 KNN imputation - Demo.mp4 | 19 MB | ||
| 003 KNN imputation - Demo_en.srt | 8.5 KB | ||
| 003 Mean or median imputation.mp4 | 25.9 MB | ||
| 003 Mean or median imputation_en.srt | 10.3 KB | ||
| 003 Outlier capping with IQR.mp4 | 41 MB | ||
| 003 Outlier capping with IQR_en.srt | 7.2 KB | ||
| 003 Standardisation Demo.mp4 | 40.3 MB | ||
| 003 Standardisation Demo_en.srt | 5.7 KB | ||
| 003 Variable Transformation with Scikit-learn.mp4 | 44.5 MB | ||
| 003 Variable Transformation with Scikit-learn_en.srt | 8 KB | ||
| 004 Arbitrary value imputation.mp4 | 30.7 MB | ||
| 004 Arbitrary value imputation_en.srt | 8.8 KB | ||
| 004 Date and time variables.mp4 | 4.2 MB | ||
| 004 Date and time variables_en.srt | 2.5 KB | ||
| 004 Equal-width discretisation Demo.mp4 | 68.2 MB | ||
| 004 Equal-width discretisation Demo_en.srt | 12.7 KB | ||
| 004 MICE.mp4 | 15.4 MB | ||
| 004 MICE_en.srt | 8.5 KB | ||
| 004 Mean normalisation.mp4 | 8.7 MB | ||
| 004 Mean normalisation_en.srt | 5 KB | ||
| 004 One-hot-encoding Demo.mp4 | 85.9 MB | ||
| 004 One-hot-encoding Demo_en.srt | 18 KB | ||
| 004 Outlier capping with mean and std.mp4 | 30.2 MB | ||
| 004 Outlier capping with mean and std_en.srt | 5.2 KB | ||
| 004 Rare labels - categorical variables.mp4 | 14.5 MB | ||
| 004 Rare labels - categorical variables_en.srt | 6.2 KB | ||
| 004 Regression pipeline.mp4 | 101.1 MB | ||
| 004 Regression pipeline_en.srt | 17.5 KB | ||
| 004 Setting up your computer.html | 3.2 KB | ||
| 004 Variable transformation with Feature-engine.mp4 | 21.6 MB | ||
| 004 Variable transformation with Feature-engine_en.srt | 4.4 KB | ||
| 005 Course material.mp4 | 5.8 MB | ||
| 005 Course material_en.srt | 2.3 KB | ||
| 005 End of distribution imputation.mp4 | 18.2 MB | ||
| 005 End of distribution imputation_en.srt | 6.1 KB | ||
| 005 Equal-frequency discretisation.mp4 | 9.4 MB | ||
| 005 Equal-frequency discretisation_en.srt | 4.9 KB | ||
| 005 Feature engineering pipeline with cross-validation.mp4 | 54.1 MB | ||
| 005 Feature engineering pipeline with cross-validation_en.srt | 8.7 KB | ||
| 005 Linear models assumptions.mp4 | 41.5 MB | ||
| 005 Linear models assumptions_en.srt | 10.9 KB | ||
| 005 Mean normalisation Demo.mp4 | 43.1 MB | ||
| 005 Mean normalisation Demo_en.srt | 6.5 KB | ||
| 005 Mixed variables.mp4 | 4.6 MB | ||
| 005 Mixed variables_en.srt | 2.8 KB | ||
| 005 One hot encoding of top categories.mp4 | 9.1 MB | ||
| 005 One hot encoding of top categories_en.srt | 3.6 KB | ||
| 005 Outlier capping with quantiles.mp4 | 10.4 MB | ||
| 005 Outlier capping with quantiles_en.srt | 3.8 KB | ||
| 005 missForest.mp4 | 2.4 MB | ||
| 005 missForest_en.srt | 1.3 KB | ||
| 005 sample-s2.csv | 9.9 MB | ||
| 006 Arbitrary capping.mp4 | 15.1 MB | ||
| 006 Arbitrary capping_en.srt | 4 KB | ||
| 006 Download Jupyter notebooks.html | 1 KB | ||
| 006 Equal-frequency discretisation Demo.mp4 | 41 MB | ||
| 006 Equal-frequency discretisation Demo_en.srt | 8 KB | ||
| 006 Frequent category imputation.mp4 | 38.1 MB | ||
| 006 Frequent category imputation_en.srt | 8.6 KB | ||
| 006 Linear model assumptions - additional reading resources (optional).html | 1.5 KB | ||
| 006 MICE and missForest - Demo.mp4 | 27.7 MB | ||
| 006 MICE and missForest - Demo_en.srt | 5.2 KB | ||
| 006 More examples.html | 307.2 B | ||
| 006 One hot encoding of top categories Demo.mp4 | 53.9 MB | ||
| 006 One hot encoding of top categories Demo_en.srt | 9.9 KB | ||
| 006 Scaling to minimum and maximum values.mp4 | 7.5 MB | ||
| 006 Scaling to minimum and maximum values_en.srt | 3.9 KB | ||
| 007 Additional reading resources (Optional).html | 1.2 KB | ||
| 007 Download datasets.html | 3.5 KB | ||
| 007 Important Feature-engine v1.0.0.html | 307.2 B | ||
| 007 K-means discretisation.mp4 | 8.4 MB | ||
| 007 K-means discretisation_en.srt | 4.7 KB | ||
| 007 MinMaxScaling Demo.mp4 | 24.9 MB | ||
| 007 MinMaxScaling Demo_en.srt | 3.5 KB | ||
| 007 Missing category imputation.mp4 | 23.4 MB | ||
| 007 Missing category imputation_en.srt | 5 KB | ||
| 007 Ordinal encoding Label encoding.mp4 | 4.9 MB | ||
| 007 Ordinal encoding Label encoding_en.srt | 2.1 KB | ||
| 007 Variable distribution.mp4 | 14.9 MB | ||
| 007 Variable distribution_en.srt | 6.5 KB | ||
| 008 Additional reading resources.html | 512 B | ||
| 008 Download presentations.html | 307.2 B | ||
| 008 K-means discretisation Demo.mp4 | 16.2 MB | ||
| 008 K-means discretisation Demo_en.srt | 3.2 KB | ||
| 008 Maximum absolute scaling.mp4 | 6.5 MB | ||
| 008 Maximum absolute scaling_en.srt | 3.4 KB | ||
| 008 Ordinal encoding Demo.mp4 | 49.5 MB | ||
| 008 Ordinal encoding Demo_en.srt | 9.9 KB | ||
| 008 Outliers.mp4 | 18.6 MB | ||
| 008 Outliers_en.srt | 10.7 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 | ||
| 009 Count or frequency encoding.mp4 | 6.9 MB | ||
| 009 Count or frequency encoding_en.srt | 3.8 KB | ||
| 009 Discretisation plus categorical encoding.mp4 | 5.9 MB | ||
| 009 Discretisation plus categorical encoding_en.srt | 3 KB | ||
| 009 MaxAbsScaling Demo.mp4 | 27.1 MB | ||
| 009 MaxAbsScaling Demo_en.srt | 4.6 KB | ||
| 009 Moving forward.mp4 | 3.9 MB | ||
| 009 Moving forward_en.srt | 2.5 KB | ||
| 009 Variable magnitude.mp4 | 7.4 MB | ||
| 009 Variable magnitude_en.srt | 4 KB | ||
| 010 Count encoding Demo.mp4 | 16.6 MB | ||
| 010 Count encoding Demo_en.srt | 5.3 KB | ||
| 010 Discretisation plus encoding Demo.mp4 | 34 MB | ||
| 010 Discretisation plus encoding Demo_en.srt | 6.5 KB | ||
| 010 FAQ Data science, Python, datasets, presentations and more.html | 2 KB | ||
| 010 Imputation with Scikit-learn.mp4 | 20.8 MB | ||
| 010 Imputation with Scikit-learn_en.srt | 5.1 KB | ||
| 010 ML-Comparison.pdf | 297.6 KB | ||
| 010 Scaling to median and quantiles.mp4 | 6.8 MB | ||
| 010 Scaling to median and quantiles_en.srt | 3.2 KB | ||
| 010 Variable characteristics and machine learning models.html | 409.6 B | ||
| 011 Additional reading resources.html | 4.5 KB | ||
| 011 Discretisation with classification trees.mp4 | 20.4 MB | ||
| 011 Discretisation with classification trees_en.srt | 5.8 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 | ||
| 011 Robust Scaling Demo.mp4 | 15.8 MB | ||
| 011 Robust Scaling Demo_en.srt | 2.4 KB | ||
| 011 Target guided ordinal encoding.mp4 | 7 MB | ||
| 011 Target guided ordinal encoding_en.srt | 3.4 KB | ||
| 012 Arbitrary value imputation with Scikit-learn.mp4 | 36.4 MB | ||
| 012 Arbitrary value imputation with Scikit-learn_en.srt | 6.4 KB | ||
| 012 Discretisation with decision trees using Scikit-learn.mp4 | 75.6 MB | ||
| 012 Discretisation with decision trees using Scikit-learn_en.srt | 13.7 KB | ||
| 012 Scaling to vector unit length.mp4 | 13.1 MB | ||
| 012 Scaling to vector unit length_en.srt | 6.8 KB | ||
| 012 Target guided ordinal encoding Demo.mp4 | 65.9 MB | ||
| 012 Target guided ordinal encoding Demo_en.srt | 9.8 KB | ||
| 013 Discretisation with decision trees using Feature-engine.mp4 | 24.8 MB | ||
| 013 Discretisation with decision trees using Feature-engine_en.srt | 4.4 KB | ||
| 013 Frequent category imputation with Scikit-learn.mp4 | 35.3 MB | ||
| 013 Frequent category imputation with Scikit-learn_en.srt | 6.7 KB | ||
| 013 Mean encoding.mp4 | 5.2 MB | ||
| 013 Mean encoding_en.srt | 2.9 KB | ||
| 013 Scaling to vector unit length Demo.mp4 | 44.8 MB | ||
| 013 Scaling to vector unit length Demo_en.srt | 6.2 KB | ||
| 014 Additional reading resources.html | 1.3 KB | ||
| 014 Domain knowledge discretisation.mp4 | 18.9 MB | ||
| 014 Domain knowledge discretisation_en.srt | 4.2 KB | ||
| 014 Mean encoding Demo.mp4 | 36.2 MB | ||
| 014 Mean encoding Demo_en.srt | 6.6 KB | ||
| 014 Missing category imputation with Scikit-learn.mp4 | 20 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 | ||
| 015 Additional reading resources.html | 1.4 KB | ||
| 015 Probability ratio encoding.mp4 | 22.6 MB | ||
| 015 Probability ratio encoding_en.srt | 7.2 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 | ||
| 016 Weight of evidence (WoE).mp4 | 10 MB | ||
| 016 Weight of evidence (WoE)_en.srt | 6.4 KB | ||
| 017 Introduction to Feature-engine.mp4 | 26.9 MB | ||
| 017 Introduction to Feature-engine_en.srt | 8.3 KB | ||
| 017 Weight of Evidence Demo.mp4 | 98.3 MB | ||
| 017 Weight of Evidence Demo_en.srt | 16.7 KB | ||
| 018 Comparison of categorical variable encoding.mp4 | 76.2 MB | ||
| 018 Comparison of categorical variable encoding_en.srt | 13.4 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 | ||
| 019 Rare label encoding.mp4 | 10.3 MB | ||
| 019 Rare label encoding_en.srt | 5.2 KB | ||
| 020 End of distribution imputation with Feature-engine.mp4 | 26 MB | ||
| 020 End of distribution imputation with Feature-engine_en.srt | 5.8 KB | ||
| 020 Rare label encoding Demo.mp4 | 60.6 MB | ||
| 020 Rare label encoding Demo_en.srt | 12.4 KB | ||
| 021 Binary encoding and feature hashing.mp4 | 13.8 MB | ||
| 021 Binary encoding and feature hashing_en.srt | 7.5 KB | ||
| 021 Frequent category imputation with Feature-engine.mp4 | 5.3 MB | ||
| 021 Frequent category imputation with Feature-engine_en.srt | 2 KB | ||
| 022 Missing category imputation with Feature-engine.mp4 | 19.8 MB | ||
| 022 Missing category imputation with Feature-engine_en.srt | 3.8 KB | ||
| 022 Summary table of encoding techniques.html | 307.2 B | ||
| 023 Additional reading resources.html | 2.4 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 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 | 307.2 B | ||
| 027 Conclusion when to use each missing data imputation method.html | 2.7 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| loan.csv | 1 MB | ||
| sample_s2.csv | 9.9 MB | ||
| ▲ 259 total files | |||
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.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
|
Udemy - Data Science Basics - Python,Stats,Feature Engineering and EDA Posted by
freecoursewb in Other
|
3.6 GB | freecoursewb | 6 months | 1 | 3 |
|
Udemy - Build A Flutter Ai Gallery App With Circle To Search Feature Posted by
freecoursewb in Other
|
2.4 GB | freecoursewb | 2 years | 2 | 3 |
| 4 GB | freecoursewb | 2 years | 2 | 9 | |
| 2.9 GB | freecoursewb | 3 years | 0 | 0 | |
| 2.1 GB | freecoursewb | 4 years | 0 | 0 |
All Comments