[Tutorialsplanet.NET] Udemy - Feature Engineering for Machine Learning

Creation Time	Aug. 18, 2023, 10:19 p.m.
Last Access Time	Sept. 6, 2024, 6:36 a.m.
File Size	3.0 GB
Keywords	Tutorialsplanet for - Feature Udemy Machine Engineering Learning NET
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Total Files	263
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13 - Assembling a feature engineering pipeline/004 Regression pipeline.mp4 101.1 MB
06 - Categorical Variable Encoding/017 Weight of Evidence Demo.mp4 98.3 MB
04 - Missing Data Imputation/008 Random sample imputation.mp4 87.6 MB
06 - Categorical Variable Encoding/004 One-hot-encoding Demo.mp4 85.9 MB
13 - Assembling a feature engineering pipeline/003 Classification pipeline.mp4 76.6 MB
06 - Categorical Variable Encoding/018 Comparison of categorical variable encoding.mp4 76.2 MB
08 - Discretisation/012 Discretisation with decision trees using Scikit-learn.mp4 75.6 MB
08 - Discretisation/004 Equal-width discretisation Demo.mp4 68.2 MB
06 - Categorical Variable Encoding/012 Target guided ordinal encoding Demo.mp4 65.9 MB
04 - Missing Data Imputation/016 Automatic determination of imputation method with Sklearn.mp4 65.4 MB
06 - Categorical Variable Encoding/020 Rare label encoding Demo.mp4 60.6 MB
13 - Assembling a feature engineering pipeline/005 Feature engineering pipeline with cross-validation.mp4 54.1 MB
06 - Categorical Variable Encoding/006 One hot encoding of top categories Demo.mp4 53.9 MB
01 - Introduction/001 Course curriculum overview.mp4 49.6 MB
06 - Categorical Variable Encoding/008 Ordinal encoding Demo.mp4 49.5 MB
10 - Feature Scaling/013 Scaling to vector unit length Demo.mp4 44.8 MB
07 - Variable Transformation/003 Variable Transformation with Scikit-learn.mp4 44.5 MB
10 - Feature Scaling/005 Mean normalisation Demo.mp4 43.1 MB
07 - Variable Transformation/002 Variable Transformation with Numpy and SciPy.mp4 42.5 MB
03 - Variable Characteristics/005 Linear models assumptions.mp4 41.5 MB
09 - Outlier Handling/003 Outlier capping with IQR.mp4 41.0 MB
08 - Discretisation/006 Equal-frequency discretisation Demo.mp4 41.0 MB
10 - Feature Scaling/003 Standardisation Demo.mp4 40.3 MB
12 - Engineering datetime variables/002 Engineering dates Demo.mp4 39.6 MB
11 - Engineering mixed variables/002 Engineering mixed variables Demo.mp4 39.5 MB
04 - Missing Data Imputation/002 Complete Case Analysis.mp4 39.2 MB
04 - Missing Data Imputation/006 Frequent category imputation.mp4 38.1 MB
04 - Missing Data Imputation/011 Mean or median imputation with Scikit-learn.mp4 37.9 MB
09 - Outlier Handling/002 Outlier trimming.mp4 37.5 MB
04 - Missing Data Imputation/025 CCA with Feature-engine.mp4 37.3 MB
04 - Missing Data Imputation/012 Arbitrary value imputation with Scikit-learn.mp4 36.4 MB
06 - Categorical Variable Encoding/014 Mean encoding Demo.mp4 36.2 MB
04 - Missing Data Imputation/013 Frequent category imputation with Scikit-learn.mp4 35.3 MB
06 - Categorical Variable Encoding/001 Categorical encoding Introduction.mp4 34.0 MB
08 - Discretisation/010 Discretisation plus encoding Demo.mp4 34.0 MB
13 - Assembling a feature engineering pipeline/001 Putting it all together.mp4 33.0 MB
09 - Outlier Handling/001 Outlier Engineering Intro.mp4 32.2 MB
04 - Missing Data Imputation/018 Mean or median imputation with Feature-engine.mp4 31.7 MB
04 - Missing Data Imputation/004 Arbitrary value imputation.mp4 30.7 MB
09 - Outlier Handling/004 Outlier capping with mean and std.mp4 30.2 MB
04 - Missing Data Imputation/024 Adding a missing indicator with Feature-engine.mp4 28.0 MB
05 - Multivariate Missing Data Imputation/006 MICE and missForest - Demo.mp4 27.7 MB
10 - Feature Scaling/009 MaxAbsScaling Demo.mp4 27.1 MB
04 - Missing Data Imputation/017 Introduction to Feature-engine.mp4 26.9 MB
04 - Missing Data Imputation/020 End of distribution imputation with Feature-engine.mp4 26.0 MB
04 - Missing Data Imputation/003 Mean or median imputation.mp4 25.9 MB
04 - Missing Data Imputation/019 Arbitrary value imputation with Feature-engine.mp4 25.1 MB
10 - Feature Scaling/007 MinMaxScaling Demo.mp4 24.9 MB
08 - Discretisation/013 Discretisation with decision trees using Feature-engine.mp4 24.8 MB
12 - Engineering datetime variables/003 Engineering time variables and different timezones.mp4 23.9 MB
04 - Missing Data Imputation/007 Missing category imputation.mp4 23.4 MB
04 - Missing Data Imputation/015 Adding a missing indicator with Scikit-learn.mp4 23.3 MB
06 - Categorical Variable Encoding/015 Probability ratio encoding.mp4 22.6 MB
03 - Variable Characteristics/003 Cardinality - categorical variables.mp4 22.5 MB
13 - Assembling a feature engineering pipeline/002 Feature Engineering Pipeline.mp4 22.0 MB
07 - Variable Transformation/004 Variable transformation with Feature-engine.mp4 21.6 MB
03 - Variable Characteristics/002 Missing data.mp4 21.5 MB
04 - Missing Data Imputation/010 Imputation with Scikit-learn.mp4 20.8 MB
01 - Introduction/002 Course requirements.mp4 20.5 MB
08 - Discretisation/011 Discretisation with classification trees.mp4 20.4 MB
04 - Missing Data Imputation/014 Missing category imputation with Scikit-learn.mp4 20.0 MB
04 - Missing Data Imputation/022 Missing category imputation with Feature-engine.mp4 19.8 MB
05 - Multivariate Missing Data Imputation/003 KNN imputation - Demo.mp4 19.0 MB
08 - Discretisation/014 Domain knowledge discretisation.mp4 18.9 MB
03 - Variable Characteristics/008 Outliers.mp4 18.6 MB
04 - Missing Data Imputation/005 End of distribution imputation.mp4 18.2 MB
04 - Missing Data Imputation/001 Introduction to missing data imputation.mp4 17.9 MB
04 - Missing Data Imputation/023 Random sample imputation with Feature-engine.mp4 16.9 MB
06 - Categorical Variable Encoding/010 Count encoding Demo.mp4 16.6 MB
08 - Discretisation/008 K-means discretisation Demo.mp4 16.2 MB
10 - Feature Scaling/011 Robust Scaling Demo.mp4 15.8 MB
08 - Discretisation/001 Discretisation Introduction.mp4 15.4 MB
05 - Multivariate Missing Data Imputation/004 MICE.mp4 15.4 MB
09 - Outlier Handling/006 Arbitrary capping.mp4 15.1 MB
03 - Variable Characteristics/007 Variable distribution.mp4 14.9 MB
02 - Variable Types/002 Numerical variables.mp4 14.8 MB
04 - Missing Data Imputation/009 Adding a missing indicator.mp4 14.7 MB
03 - Variable Characteristics/004 Rare labels - categorical variables.mp4 14.5 MB
06 - Categorical Variable Encoding/021 Binary encoding and feature hashing.mp4 13.8 MB
06 - Categorical Variable Encoding/002 One hot encoding.mp4 13.7 MB
12 - Engineering datetime variables/001 Engineering datetime variables.mp4 13.4 MB
10 - Feature Scaling/012 Scaling to vector unit length.mp4 13.1 MB
11 - Engineering mixed variables/001 Engineering mixed variables.mp4 11.7 MB
10 - Feature Scaling/002 Standardisation.mp4 11.6 MB
09 - Outlier Handling/005 Outlier capping with quantiles.mp4 10.4 MB
06 - Categorical Variable Encoding/019 Rare label encoding.mp4 10.3 MB
06 - Categorical Variable Encoding/016 Weight of evidence (WoE).mp4 10.0 MB
02 - Variable Types/005 sample-s2.csv 9.9 MB
05 - Multivariate Missing Data Imputation/002 KNN imputation.mp4 9.5 MB
08 - Discretisation/005 Equal-frequency discretisation.mp4 9.4 MB
07 - Variable Transformation/001 Variable Transformation Introduction.mp4 9.3 MB
10 - Feature Scaling/001 Feature scaling Introduction.mp4 9.1 MB
06 - Categorical Variable Encoding/005 One hot encoding of top categories.mp4 9.1 MB
08 - Discretisation/002 Equal-width discretisation.mp4 9.1 MB
10 - Feature Scaling/004 Mean normalisation.mp4 8.7 MB
08 - Discretisation/007 K-means discretisation.mp4 8.4 MB
02 - Variable Types/003 Categorical variables.mp4 7.6 MB
05 - Multivariate Missing Data Imputation/001 Multivariate imputation.mp4 7.5 MB
10 - Feature Scaling/006 Scaling to minimum and maximum values.mp4 7.5 MB
03 - Variable Characteristics/009 Variable magnitude.mp4 7.4 MB
03 - Variable Characteristics/001 Variable characteristics.mp4 7.2 MB
06 - Categorical Variable Encoding/011 Target guided ordinal encoding.mp4 7.0 MB
06 - Categorical Variable Encoding/009 Count or frequency encoding.mp4 6.9 MB
10 - Feature Scaling/010 Scaling to median and quantiles.mp4 6.8 MB
10 - Feature Scaling/008 Maximum absolute scaling.mp4 6.5 MB
08 - Discretisation/009 Discretisation plus categorical encoding.mp4 5.9 MB
01 - Introduction/005 Course material.mp4 5.8 MB
02 - Variable Types/001 Variables Intro.mp4 5.4 MB
04 - Missing Data Imputation/021 Frequent category imputation with Feature-engine.mp4 5.3 MB
06 - Categorical Variable Encoding/013 Mean encoding.mp4 5.2 MB
06 - Categorical Variable Encoding/007 Ordinal encoding Label encoding.mp4 4.9 MB
02 - Variable Types/005 Mixed variables.mp4 4.6 MB
02 - Variable Types/004 Date and time variables.mp4 4.2 MB
01 - Introduction/009 Moving forward.mp4 3.9 MB
01 - Introduction/007 Datasets.zip 3.4 MB
05 - Multivariate Missing Data Imputation/005 missForest.mp4 2.4 MB
03 - Variable Characteristics/010 ML-Comparison.pdf 297.6 KB
04 - Missing Data Imputation/026 NA-methods-Comparison.pdf 273.8 KB
04 - Missing Data Imputation/008 Random sample imputation_en.srt 18.2 KB
06 - Categorical Variable Encoding/004 One-hot-encoding Demo_en.srt 18.0 KB
13 - Assembling a feature engineering pipeline/004 Regression pipeline_en.srt 17.5 KB
06 - Categorical Variable Encoding/017 Weight of Evidence Demo_en.srt 16.7 KB
13 - Assembling a feature engineering pipeline/003 Classification pipeline_en.srt 16.6 KB
08 - Discretisation/012 Discretisation with decision trees using Scikit-learn_en.srt 13.7 KB
06 - Categorical Variable Encoding/018 Comparison of categorical variable encoding_en.srt 13.4 KB
08 - Discretisation/004 Equal-width discretisation Demo_en.srt 12.7 KB
06 - Categorical Variable Encoding/020 Rare label encoding Demo_en.srt 12.4 KB
03 - Variable Characteristics/005 Linear models assumptions_en.srt 10.9 KB
13 - Assembling a feature engineering pipeline/002 Feature Engineering Pipeline_en.srt 10.7 KB
03 - Variable Characteristics/008 Outliers_en.srt 10.7 KB
04 - Missing Data Imputation/003 Mean or median imputation_en.srt 10.3 KB
06 - Categorical Variable Encoding/006 One hot encoding of top categories Demo_en.srt 9.9 KB
06 - Categorical Variable Encoding/008 Ordinal encoding Demo_en.srt 9.9 KB
06 - Categorical Variable Encoding/012 Target guided ordinal encoding Demo_en.srt 9.8 KB
12 - Engineering datetime variables/002 Engineering dates Demo_en.srt 9.5 KB
04 - Missing Data Imputation/016 Automatic determination of imputation method with Sklearn_en.srt 9.2 KB
03 - Variable Characteristics/002 Missing data_en.srt 9.0 KB
13 - Assembling a feature engineering pipeline/001 Putting it all together_en.srt 8.9 KB
04 - Missing Data Imputation/004 Arbitrary value imputation_en.srt 8.8 KB
13 - Assembling a feature engineering pipeline/005 Feature engineering pipeline with cross-validation_en.srt 8.7 KB
07 - Variable Transformation/002 Variable Transformation with Numpy and SciPy_en.srt 8.7 KB
04 - Missing Data Imputation/002 Complete Case Analysis_en.srt 8.6 KB
04 - Missing Data Imputation/006 Frequent category imputation_en.srt 8.6 KB
05 - Multivariate Missing Data Imputation/003 KNN imputation - Demo_en.srt 8.5 KB
05 - Multivariate Missing Data Imputation/004 MICE_en.srt 8.5 KB
04 - Missing Data Imputation/025 CCA with Feature-engine_en.srt 8.5 KB
09 - Outlier Handling/002 Outlier trimming_en.srt 8.5 KB
04 - Missing Data Imputation/017 Introduction to Feature-engine_en.srt 8.3 KB
06 - Categorical Variable Encoding/001 Categorical encoding Introduction_en.srt 8.3 KB
07 - Variable Transformation/003 Variable Transformation with Scikit-learn_en.srt 8.0 KB
09 - Outlier Handling/001 Outlier Engineering Intro_en.srt 8.0 KB
08 - Discretisation/006 Equal-frequency discretisation Demo_en.srt 8.0 KB
11 - Engineering mixed variables/002 Engineering mixed variables Demo_en.srt 7.7 KB
06 - Categorical Variable Encoding/021 Binary encoding and feature hashing_en.srt 7.5 KB
06 - Categorical Variable Encoding/002 One hot encoding_en.srt 7.2 KB
06 - Categorical Variable Encoding/015 Probability ratio encoding_en.srt 7.2 KB
09 - Outlier Handling/003 Outlier capping with IQR_en.srt 7.2 KB
02 - Variable Types/002 Numerical variables_en.srt 7.0 KB
01 - Introduction/001 Course curriculum overview_en.srt 7.0 KB
04 - Missing Data Imputation/009 Adding a missing indicator_en.srt 6.9 KB
10 - Feature Scaling/012 Scaling to vector unit length_en.srt 6.8 KB
04 - Missing Data Imputation/013 Frequent category imputation with Scikit-learn_en.srt 6.7 KB
10 - Feature Scaling/002 Standardisation_en.srt 6.7 KB
06 - Categorical Variable Encoding/014 Mean encoding Demo_en.srt 6.6 KB
08 - Discretisation/010 Discretisation plus encoding Demo_en.srt 6.5 KB
10 - Feature Scaling/005 Mean normalisation Demo_en.srt 6.5 KB
04 - Missing Data Imputation/011 Mean or median imputation with Scikit-learn_en.srt 6.5 KB
03 - Variable Characteristics/007 Variable distribution_en.srt 6.5 KB
06 - Categorical Variable Encoding/016 Weight of evidence (WoE)_en.srt 6.4 KB
04 - Missing Data Imputation/012 Arbitrary value imputation with Scikit-learn_en.srt 6.4 KB
03 - Variable Characteristics/003 Cardinality - categorical variables_en.srt 6.4 KB
03 - Variable Characteristics/004 Rare labels - categorical variables_en.srt 6.2 KB
10 - Feature Scaling/013 Scaling to vector unit length Demo_en.srt 6.2 KB
04 - Missing Data Imputation/005 End of distribution imputation_en.srt 6.1 KB
04 - Missing Data Imputation/020 End of distribution imputation with Feature-engine_en.srt 5.8 KB
08 - Discretisation/011 Discretisation with classification trees_en.srt 5.8 KB
12 - Engineering datetime variables/003 Engineering time variables and different timezones_en.srt 5.7 KB
10 - Feature Scaling/003 Standardisation Demo_en.srt 5.7 KB
07 - Variable Transformation/001 Variable Transformation Introduction_en.srt 5.6 KB
12 - Engineering datetime variables/001 Engineering datetime variables_en.srt 5.6 KB
04 - Missing Data Imputation/018 Mean or median imputation with Feature-engine_en.srt 5.5 KB
06 - Categorical Variable Encoding/010 Count encoding Demo_en.srt 5.3 KB
04 - Missing Data Imputation/001 Introduction to missing data imputation_en.srt 5.2 KB
06 - Categorical Variable Encoding/019 Rare label encoding_en.srt 5.2 KB
05 - Multivariate Missing Data Imputation/006 MICE and missForest - Demo_en.srt 5.2 KB
09 - Outlier Handling/004 Outlier capping with mean and std_en.srt 5.2 KB
04 - Missing Data Imputation/010 Imputation with Scikit-learn_en.srt 5.1 KB
10 - Feature Scaling/004 Mean normalisation_en.srt 5.0 KB
04 - Missing Data Imputation/007 Missing category imputation_en.srt 5.0 KB
05 - Multivariate Missing Data Imputation/002 KNN imputation_en.srt 4.9 KB
08 - Discretisation/005 Equal-frequency discretisation_en.srt 4.9 KB
04 - Missing Data Imputation/024 Adding a missing indicator with Feature-engine_en.srt 4.9 KB
10 - Feature Scaling/001 Feature scaling Introduction_en.srt 4.7 KB
08 - Discretisation/007 K-means discretisation_en.srt 4.7 KB
04 - Missing Data Imputation/015 Adding a missing indicator with Scikit-learn_en.srt 4.6 KB
02 - Variable Types/003 Categorical variables_en.srt 4.6 KB
10 - Feature Scaling/009 MaxAbsScaling Demo_en.srt 4.6 KB
03 - Variable Characteristics/011 Additional reading resources.html 4.5 KB
08 - Discretisation/002 Equal-width discretisation_en.srt 4.5 KB
08 - Discretisation/013 Discretisation with decision trees using Feature-engine_en.srt 4.4 KB
07 - Variable Transformation/004 Variable transformation with Feature-engine_en.srt 4.4 KB
08 - Discretisation/014 Domain knowledge discretisation_en.srt 4.2 KB
03 - Variable Characteristics/009 Variable magnitude_en.srt 4.0 KB
11 - Engineering mixed variables/001 Engineering mixed variables_en.srt 4.0 KB
09 - Outlier Handling/006 Arbitrary capping_en.srt 4.0 KB
05 - Multivariate Missing Data Imputation/001 Multivariate imputation_en.srt 3.9 KB
10 - Feature Scaling/006 Scaling to minimum and maximum values_en.srt 3.9 KB
06 - Categorical Variable Encoding/009 Count or frequency encoding_en.srt 3.8 KB
09 - Outlier Handling/005 Outlier capping with quantiles_en.srt 3.8 KB
04 - Missing Data Imputation/022 Missing category imputation with Feature-engine_en.srt 3.8 KB
04 - Missing Data Imputation/019 Arbitrary value imputation with Feature-engine_en.srt 3.8 KB
06 - Categorical Variable Encoding/005 One hot encoding of top categories_en.srt 3.6 KB
04 - Missing Data Imputation/014 Missing category imputation with Scikit-learn_en.srt 3.6 KB
03 - Variable Characteristics/001 Variable characteristics_en.srt 3.5 KB
10 - Feature Scaling/007 MinMaxScaling Demo_en.srt 3.5 KB
02 - Variable Types/001 Variables Intro_en.srt 3.5 KB
01 - Introduction/007 Download datasets.html 3.5 KB
01 - Introduction/002 Course requirements_en.srt 3.5 KB
08 - Discretisation/001 Discretisation Introduction_en.srt 3.5 KB
06 - Categorical Variable Encoding/011 Target guided ordinal encoding_en.srt 3.4 KB
10 - Feature Scaling/008 Maximum absolute scaling_en.srt 3.4 KB
10 - Feature Scaling/010 Scaling to median and quantiles_en.srt 3.2 KB
08 - Discretisation/008 K-means discretisation Demo_en.srt 3.2 KB
01 - Introduction/004 Setting up your computer.html 3.2 KB
08 - Discretisation/009 Discretisation plus categorical encoding_en.srt 3.0 KB
06 - Categorical Variable Encoding/013 Mean encoding_en.srt 2.9 KB
04 - Missing Data Imputation/023 Random sample imputation with Feature-engine_en.srt 2.9 KB
02 - Variable Types/005 Mixed variables_en.srt 2.8 KB
04 - Missing Data Imputation/027 Conclusion when to use each missing data imputation method.html 2.7 KB
01 - Introduction/009 Moving forward_en.srt 2.5 KB
02 - Variable Types/004 Date and time variables_en.srt 2.5 KB
10 - Feature Scaling/011 Robust Scaling Demo_en.srt 2.4 KB
06 - Categorical Variable Encoding/023 Additional reading resources.html 2.4 KB
01 - Introduction/005 Course material_en.srt 2.3 KB
06 - Categorical Variable Encoding/007 Ordinal encoding Label encoding_en.srt 2.1 KB
04 - Missing Data Imputation/021 Frequent category imputation with Feature-engine_en.srt 2.0 KB
01 - Introduction/010 FAQ Data science, Python, datasets, presentations and more.html 2.0 KB
01 - Introduction/003 How to approach this course.html 1.7 KB
03 - Variable Characteristics/006 Linear model assumptions - additional reading resources (optional).html 1.5 KB
08 - Discretisation/015 Additional reading resources.html 1.4 KB
10 - Feature Scaling/014 Additional reading resources.html 1.3 KB
05 - Multivariate Missing Data Imputation/005 missForest_en.srt 1.3 KB
05 - Multivariate Missing Data Imputation/007 Additional reading resources (Optional).html 1.2 KB
01 - Introduction/006 Download Jupyter notebooks.html 1019 bytes
06 - Categorical Variable Encoding/003 Important Feature-engine version 1.0.0.html 1009 bytes
14 - Final section Next steps/001 Survey.html 947 bytes
08 - Discretisation/003 Important Feature-engine v 1.0.0.html 739 bytes
14 - Final section Next steps/003 Bonus lecture.html 625 bytes
14 - Final section Next steps/002 Congratulations.html 593 bytes
09 - Outlier Handling/008 Additional reading resources.html 526 bytes
03 - Variable Characteristics/010 Variable characteristics and machine learning models.html 402 bytes
04 - Missing Data Imputation/026 Overview of missing value imputation methods.html 339 bytes
06 - Categorical Variable Encoding/022 Summary table of encoding techniques.html 312 bytes
13 - Assembling a feature engineering pipeline/006 More examples.html 308 bytes
01 - Introduction/008 Download presentations.html 286 bytes
09 - Outlier Handling/007 Important Feature-engine v1.0.0.html 262 bytes
0. Websites you may like/[Tutorialsplanet.NET].url 128 bytes
03 - Variable Characteristics/[Tutorialsplanet.NET].url 128 bytes
05 - Multivariate Missing Data Imputation/[Tutorialsplanet.NET].url 128 bytes
07 - Variable Transformation/[Tutorialsplanet.NET].url 128 bytes
11 - Engineering mixed variables/[Tutorialsplanet.NET].url 128 bytes
13 - Assembling a feature engineering pipeline/[Tutorialsplanet.NET].url 128 bytes
[Tutorialsplanet.NET].url 128 bytes

[Tutorialsplanet.NET] Udemy - Feature Engineering for Machine Learning

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