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[ DevCourseWeb com ] Udemy - Feature Engineering for Machine Learning by Soledad Galli
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01 - Introduction
001 Course curriculum overview.mp4
001 Course curriculum overview_en.srt
002 Course requirements.mp4
002 Course requirements_en.srt
003 How to approach this course.html
004 Setting up your computer.html
005 Course material.mp4
005 Course material_en.srt
006 Download Jupyter notebooks.html
007 Download datasets.html
008 Download presentations.html
009 Moving forward.mp4
009 Moving forward_en.srt
010 FAQ Data science, Python, datasets, presentations and more.html
loan.csv
sample_s2.csv
02 - Variable Types
001 Variables Intro.mp4
001 Variables Intro_en.srt
002 Numerical variables.mp4
002 Numerical variables_en.srt
003 Categorical variables.mp4
003 Categorical variables_en.srt
004 Date and time variables.mp4
004 Date and time variables_en.srt
005 Mixed variables.mp4
005 Mixed variables_en.srt
005 sample-s2.csv
03 - Variable Characteristics
001 Variable characteristics.mp4
001 Variable characteristics_en.srt
002 Missing data.mp4
002 Missing data_en.srt
003 Cardinality - categorical variables.mp4
003 Cardinality - categorical variables_en.srt
004 Rare labels - categorical variables.mp4
004 Rare labels - categorical variables_en.srt
005 Linear models assumptions.mp4
005 Linear models assumptions_en.srt
006 Linear model assumptions - additional reading resources (optional).html
007 Variable distribution.mp4
007 Variable distribution_en.srt
008 Outliers.mp4
008 Outliers_en.srt
009 Variable magnitude.mp4
009 Variable magnitude_en.srt
010 ML-Comparison.pdf
010 Variable characteristics and machine learning models.html
011 Additional reading resources.html
04 - Missing Data Imputation
001 Introduction to missing data imputation.mp4
001 Introduction to missing data imputation_en.srt
002 Complete Case Analysis.mp4
002 Complete Case Analysis_en.srt
003 Mean or median imputation.mp4
003 Mean or median imputation_en.srt
004 Arbitrary value imputation.mp4
004 Arbitrary value imputation_en.srt
005 End of distribution imputation.mp4
005 End of distribution imputation_en.srt
006 Frequent category imputation.mp4
006 Frequent category imputation_en.srt
007 Missing category imputation.mp4
007 Missing category imputation_en.srt
008 Random sample imputation.mp4
008 Random sample imputation_en.srt
009 Adding a missing indicator.mp4
009 Adding a missing indicator_en.srt
010 Imputation with Scikit-learn.mp4
010 Imputation with Scikit-learn_en.srt
011 Mean or median imputation with Scikit-learn.mp4
011 Mean or median imputation with Scikit-learn_en.srt
012 Arbitrary value imputation with Scikit-learn.mp4
012 Arbitrary value imputation with Scikit-learn_en.srt
013 Frequent category imputation with Scikit-learn.mp4
013 Frequent category imputation with Scikit-learn_en.srt
014 Missing category imputation with Scikit-learn.mp4
014 Missing category imputation with Scikit-learn_en.srt
015 Adding a missing indicator with Scikit-learn.mp4
015 Adding a missing indicator with Scikit-learn_en.srt
016 Automatic determination of imputation method with Sklearn.mp4
016 Automatic determination of imputation method with Sklearn_en.srt
017 Introduction to Feature-engine.mp4
017 Introduction to Feature-engine_en.srt
018 Mean or median imputation with Feature-engine.mp4
018 Mean or median imputation with Feature-engine_en.srt
019 Arbitrary value imputation with Feature-engine.mp4
019 Arbitrary value imputation with Feature-engine_en.srt
020 End of distribution imputation with Feature-engine.mp4
020 End of distribution imputation with Feature-engine_en.srt
021 Frequent category imputation with Feature-engine.mp4
021 Frequent category imputation with Feature-engine_en.srt
022 Missing category imputation with Feature-engine.mp4
022 Missing category imputation with Feature-engine_en.srt
023 Random sample imputation with Feature-engine.mp4
023 Random sample imputation with Feature-engine_en.srt
024 Adding a missing indicator with Feature-engine.mp4
024 Adding a missing indicator with Feature-engine_en.srt
025 CCA with Feature-engine.mp4
025 CCA with Feature-engine_en.srt
026 NA-methods-Comparison.pdf
026 Overview of missing value imputation methods.html
027 Conclusion when to use each missing data imputation method.html
05 - Multivariate Missing Data Imputation
001 Multivariate imputation.mp4
001 Multivariate imputation_en.srt
002 KNN imputation.mp4
002 KNN imputation_en.srt
003 KNN imputation - Demo.mp4
003 KNN imputation - Demo_en.srt
004 MICE.mp4
004 MICE_en.srt
005 missForest.mp4
005 missForest_en.srt
006 MICE and missForest - Demo.mp4
006 MICE and missForest - Demo_en.srt
007 Additional reading resources (Optional).html
06 - Categorical Variable Encoding
001 Categorical encoding Introduction.mp4
001 Categorical encoding Introduction_en.srt
002 One hot encoding.mp4
002 One hot encoding_en.srt
003 Important Feature-engine version 1.0.0.html
004 One-hot-encoding Demo.mp4
004 One-hot-encoding Demo_en.srt
005 One hot encoding of top categories.mp4
005 One hot encoding of top categories_en.srt
006 One hot encoding of top categories Demo.mp4
006 One hot encoding of top categories Demo_en.srt
007 Ordinal encoding Label encoding.mp4
007 Ordinal encoding Label encoding_en.srt
008 Ordinal encoding Demo.mp4
008 Ordinal encoding Demo_en.srt
009 Count or frequency encoding.mp4
009 Count or frequency encoding_en.srt
010 Count encoding Demo.mp4
010 Count encoding Demo_en.srt
011 Target guided ordinal encoding.mp4
011 Target guided ordinal encoding_en.srt
012 Target guided ordinal encoding Demo.mp4
012 Target guided ordinal encoding Demo_en.srt
013 Mean encoding.mp4
013 Mean encoding_en.srt
014 Mean encoding Demo.mp4
014 Mean encoding Demo_en.srt
015 Probability ratio encoding.mp4
015 Probability ratio encoding_en.srt
016 Weight of evidence (WoE).mp4
016 Weight of evidence (WoE)_en.srt
017 Weight of Evidence Demo.mp4
017 Weight of Evidence Demo_en.srt
018 Comparison of categorical variable encoding.mp4
018 Comparison of categorical variable encoding_en.srt
019 Rare label encoding.mp4
019 Rare label encoding_en.srt
020 Rare label encoding Demo.mp4
020 Rare label encoding Demo_en.srt
021 Binary encoding and feature hashing.mp4
021 Binary encoding and feature hashing_en.srt
022 Summary table of encoding techniques.html
023 Additional reading resources.html
07 - Variable Transformation
001 Variable Transformation Introduction.mp4
001 Variable Transformation Introduction_en.srt
002 Variable Transformation with Numpy and SciPy.mp4
002 Variable Transformation with Numpy and SciPy_en.srt
003 Variable Transformation with Scikit-learn.mp4
003 Variable Transformation with Scikit-learn_en.srt
004 Variable transformation with Feature-engine.mp4
004 Variable transformation with Feature-engine_en.srt
08 - Discretisation
001 Discretisation Introduction.mp4
001 Discretisation Introduction_en.srt
002 Equal-width discretisation.mp4
002 Equal-width discretisation_en.srt
003 Important Feature-engine v 1.0.0.html
004 Equal-width discretisation Demo.mp4
004 Equal-width discretisation Demo_en.srt
005 Equal-frequency discretisation.mp4
005 Equal-frequency discretisation_en.srt
006 Equal-frequency discretisation Demo.mp4
006 Equal-frequency discretisation Demo_en.srt
007 K-means discretisation.mp4
007 K-means discretisation_en.srt
008 K-means discretisation Demo.mp4
008 K-means discretisation Demo_en.srt
009 Discretisation plus categorical encoding.mp4
009 Discretisation plus categorical encoding_en.srt
010 Discretisation plus encoding Demo.mp4
010 Discretisation plus encoding Demo_en.srt
011 Discretisation with classification trees.mp4
011 Discretisation with classification trees_en.srt
012 Discretisation with decision trees using Scikit-learn.mp4
012 Discretisation with decision trees using Scikit-learn_en.srt
013 Discretisation with decision trees using Feature-engine.mp4
013 Discretisation with decision trees using Feature-engine_en.srt
014 Domain knowledge discretisation.mp4
014 Domain knowledge discretisation_en.srt
015 Additional reading resources.html
09 - Outlier Handling
001 Outlier Engineering Intro.mp4
001 Outlier Engineering Intro_en.srt
002 Outlier trimming.mp4
002 Outlier trimming_en.srt
003 Outlier capping with IQR.mp4
003 Outlier capping with IQR_en.srt
004 Outlier capping with mean and std.mp4
004 Outlier capping with mean and std_en.srt
005 Outlier capping with quantiles.mp4
005 Outlier capping with quantiles_en.srt
006 Arbitrary capping.mp4
006 Arbitrary capping_en.srt
007 Important Feature-engine v1.0.0.html
008 Additional reading resources.html
10 - Feature Scaling
001 Feature scaling Introduction.mp4
001 Feature scaling Introduction_en.srt
002 Standardisation.mp4
002 Standardisation_en.srt
003 Standardisation Demo.mp4
003 Standardisation Demo_en.srt
004 Mean normalisation.mp4
004 Mean normalisation_en.srt
005 Mean normalisation Demo.mp4
005 Mean normalisation Demo_en.srt
006 Scaling to minimum and maximum values.mp4
006 Scaling to minimum and maximum values_en.srt
007 MinMaxScaling Demo.mp4
007 MinMaxScaling Demo_en.srt
008 Maximum absolute scaling.mp4
008 Maximum absolute scaling_en.srt
009 MaxAbsScaling Demo.mp4
009 MaxAbsScaling Demo_en.srt
010 Scaling to median and quantiles.mp4
010 Scaling to median and quantiles_en.srt
011 Robust Scaling Demo.mp4
011 Robust Scaling Demo_en.srt
012 Scaling to vector unit length.mp4
012 Scaling to vector unit length_en.srt
013 Scaling to vector unit length Demo.mp4
013 Scaling to vector unit length Demo_en.srt
014 Additional reading resources.html
11 - Engineering mixed variables
001 Engineering mixed variables.mp4
001 Engineering mixed variables_en.srt
002 Engineering mixed variables Demo.mp4
002 Engineering mixed variables Demo_en.srt
12 - Engineering datetime variables
001 Engineering datetime variables.mp4
001 Engineering datetime variables_en.srt
002 Engineering dates Demo.mp4
002 Engineering dates Demo_en.srt
003 Engineering time variables and different timezones.mp4
003 Engineering time variables and different timezones_en.srt
13 - Assembling a feature engineering pipeline
001 Putting it all together.mp4
001 Putting it all together_en.srt
002 Feature Engineering Pipeline.mp4
002 Feature Engineering Pipeline_en.srt
003 Classification pipeline.mp4
003 Classification pipeline_en.srt
004 Regression pipeline.mp4
004 Regression pipeline_en.srt
005 Feature engineering pipeline with cross-validation.mp4
005 Feature engineering pipeline with cross-validation_en.srt
006 More examples.html
14 - Final section Next steps
001 Survey.html
002 Congratulations.html
003 Bonus lecture.html
Bonus Resources.txt
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