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001 Course curriculum overview.mp4
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001 Putting it all together.mp4
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001 Survey.html
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001 Variable Transformation Introduction.mp4
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002 Complete Case Analysis.mp4
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002 Congratulations.html
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002 Course requirements.mp4
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002 Engineering mixed variables Demo.mp4
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002 Equal-width discretisation.mp4
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002 Feature Engineering Pipeline.mp4
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002 KNN imputation.mp4
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002 Variable Transformation with Numpy and SciPy.mp4
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003 Bonus lecture.html
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003 Cardinality - categorical variables.mp4
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003 How to approach this course.html
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003 Important Feature-engine v 1.0.0.html
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003 KNN imputation - Demo.mp4
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003 Variable Transformation with Scikit-learn.mp4
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004 Arbitrary value imputation.mp4
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004 Date and time variables.mp4
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004 Outlier capping with mean and std.mp4
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004 Setting up your computer.html
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004 Variable transformation with Feature-engine.mp4
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005 Equal-frequency discretisation.mp4
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005 Feature engineering pipeline with cross-validation.mp4
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005 Linear models assumptions.mp4
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005 Mean normalisation Demo.mp4
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005 Mixed variables.mp4
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005 One hot encoding of top categories.mp4
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005 Outlier capping with quantiles.mp4
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005 sample-s2.csv
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006 Arbitrary capping.mp4
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006 Download Jupyter notebooks.html
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006 Equal-frequency discretisation Demo.mp4
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006 Frequent category imputation.mp4
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006 Linear model assumptions - additional reading resources (optional).html
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006 MICE and missForest - Demo.mp4
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006 More examples.html
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006 One hot encoding of top categories Demo.mp4
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006 Scaling to minimum and maximum values.mp4
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007 Download datasets.html
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007 Important Feature-engine v1.0.0.html
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007 K-means discretisation.mp4
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007 MinMaxScaling Demo.mp4
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007 Missing category imputation.mp4
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007 Ordinal encoding Label encoding.mp4
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007 Variable distribution.mp4
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008 Additional reading resources.html
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008 Download presentations.html
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008 K-means discretisation Demo.mp4
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008 Maximum absolute scaling.mp4
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008 Ordinal encoding Demo.mp4
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008 Outliers.mp4
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008 Random sample imputation.mp4
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009 Adding a missing indicator.mp4
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009 Count or frequency encoding.mp4
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010 Count encoding Demo.mp4
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010 Discretisation plus encoding Demo.mp4
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010 FAQ Data science, Python, datasets, presentations and more.html
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010 Imputation with Scikit-learn.mp4
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010 ML-Comparison.pdf
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010 Scaling to median and quantiles.mp4
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011 Additional reading resources.html
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011 Discretisation with classification trees.mp4
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011 Robust Scaling Demo.mp4
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011 Target guided ordinal encoding.mp4
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012 Arbitrary value imputation with Scikit-learn.mp4
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012 Discretisation with decision trees using Scikit-learn.mp4
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013 Discretisation with decision trees using Feature-engine.mp4
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013 Frequent category imputation with Scikit-learn.mp4
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014 Additional reading resources.html
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014 Domain knowledge discretisation.mp4
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015 Probability ratio encoding.mp4
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016 Automatic determination of imputation method with Sklearn.mp4
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016 Automatic determination of imputation method with Sklearn_en.srt
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017 Introduction to Feature-engine.mp4
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017 Introduction to Feature-engine_en.srt
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SRT
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8.3 KB
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017 Weight of Evidence Demo.mp4
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MP4
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98.3 MB
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017 Weight of Evidence Demo_en.srt
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SRT
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16.7 KB
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018 Comparison of categorical variable encoding.mp4
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MP4
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76.2 MB
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018 Comparison of categorical variable encoding_en.srt
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SRT
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13.4 KB
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018 Mean or median imputation with Feature-engine.mp4
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MP4
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31.7 MB
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018 Mean or median imputation with Feature-engine_en.srt
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SRT
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5.5 KB
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019 Arbitrary value imputation with Feature-engine.mp4
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MP4
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25.1 MB
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019 Arbitrary value imputation with Feature-engine_en.srt
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SRT
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3.8 KB
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019 Rare label encoding.mp4
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MP4
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10.3 MB
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019 Rare label encoding_en.srt
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SRT
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5.2 KB
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020 End of distribution imputation with Feature-engine.mp4
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MP4
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26 MB
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020 End of distribution imputation with Feature-engine_en.srt
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SRT
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5.8 KB
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020 Rare label encoding Demo.mp4
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MP4
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60.6 MB
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020 Rare label encoding Demo_en.srt
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SRT
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12.4 KB
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021 Binary encoding and feature hashing.mp4
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MP4
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13.8 MB
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021 Binary encoding and feature hashing_en.srt
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SRT
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7.5 KB
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021 Frequent category imputation with Feature-engine.mp4
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MP4
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5.3 MB
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021 Frequent category imputation with Feature-engine_en.srt
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SRT
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2 KB
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022 Missing category imputation with Feature-engine.mp4
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MP4
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19.8 MB
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022 Missing category imputation with Feature-engine_en.srt
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SRT
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3.8 KB
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022 Summary table of encoding techniques.html
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HTML
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307.2 B
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023 Additional reading resources.html
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HTML
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2.4 KB
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023 Random sample imputation with Feature-engine.mp4
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MP4
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16.9 MB
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023 Random sample imputation with Feature-engine_en.srt
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SRT
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2.9 KB
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024 Adding a missing indicator with Feature-engine.mp4
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MP4
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28 MB
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024 Adding a missing indicator with Feature-engine_en.srt
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SRT
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4.9 KB
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025 CCA with Feature-engine.mp4
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MP4
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37.3 MB
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025 CCA with Feature-engine_en.srt
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SRT
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8.5 KB
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026 NA-methods-Comparison.pdf
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PDF
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273.8 KB
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026 Overview of missing value imputation methods.html
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HTML
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307.2 B
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027 Conclusion when to use each missing data imputation method.html
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HTML
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2.7 KB
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Bonus Resources.txt
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TXT
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409.6 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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loan.csv
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CSV
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1 MB
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sample_s2.csv
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CSV
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9.9 MB
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