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Please note that this page does not hosts or makes available any of the listed filenames. You
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| 001 Categorical encoding Introduction_en.srt |
8.27KB |
| 001 Categorical encoding Introduction.mp4 |
34.02MB |
| 001 Course curriculum overview_en.srt |
6.95KB |
| 001 Course curriculum overview.mp4 |
49.62MB |
| 001 Discretisation Introduction_en.srt |
3.45KB |
| 001 Discretisation Introduction.mp4 |
15.45MB |
| 001 Engineering datetime variables_en.srt |
5.55KB |
| 001 Engineering datetime variables.mp4 |
13.42MB |
| 001 Engineering mixed variables_en.srt |
4.02KB |
| 001 Engineering mixed variables.mp4 |
11.72MB |
| 001 Feature scaling Introduction_en.srt |
4.71KB |
| 001 Feature scaling Introduction.mp4 |
9.15MB |
| 001 Introduction to missing data imputation_en.srt |
5.21KB |
| 001 Introduction to missing data imputation.mp4 |
17.87MB |
| 001 Multivariate imputation_en.srt |
3.87KB |
| 001 Multivariate imputation.mp4 |
7.48MB |
| 001 Outlier Engineering Intro_en.srt |
8.00KB |
| 001 Outlier Engineering Intro.mp4 |
32.21MB |
| 001 Putting it all together_en.srt |
8.89KB |
| 001 Putting it all together.mp4 |
32.96MB |
| 001 Survey.html |
947B |
| 001 Variable characteristics_en.srt |
3.55KB |
| 001 Variable characteristics.mp4 |
7.21MB |
| 001 Variables Intro_en.srt |
3.50KB |
| 001 Variables Intro.mp4 |
5.38MB |
| 001 Variable Transformation Introduction_en.srt |
5.59KB |
| 001 Variable Transformation Introduction.mp4 |
9.28MB |
| 002 Complete Case Analysis_en.srt |
8.63KB |
| 002 Complete Case Analysis.mp4 |
39.23MB |
| 002 Congratulations.html |
593B |
| 002 Course requirements_en.srt |
3.45KB |
| 002 Course requirements.mp4 |
20.53MB |
| 002 Engineering dates Demo_en.srt |
9.47KB |
| 002 Engineering dates Demo.mp4 |
39.65MB |
| 002 Engineering mixed variables Demo_en.srt |
7.70KB |
| 002 Engineering mixed variables Demo.mp4 |
39.48MB |
| 002 Equal-width discretisation_en.srt |
4.50KB |
| 002 Equal-width discretisation.mp4 |
9.05MB |
| 002 Feature Engineering Pipeline_en.srt |
10.73KB |
| 002 Feature Engineering Pipeline.mp4 |
22.04MB |
| 002 KNN imputation_en.srt |
4.92KB |
| 002 KNN imputation.mp4 |
9.55MB |
| 002 Missing data_en.srt |
8.98KB |
| 002 Missing data.mp4 |
21.49MB |
| 002 Numerical variables_en.srt |
7.03KB |
| 002 Numerical variables.mp4 |
14.77MB |
| 002 One hot encoding_en.srt |
7.23KB |
| 002 One hot encoding.mp4 |
13.69MB |
| 002 Outlier trimming_en.srt |
8.45KB |
| 002 Outlier trimming.mp4 |
37.55MB |
| 002 Standardisation_en.srt |
6.71KB |
| 002 Standardisation.mp4 |
11.64MB |
| 002 Variable Transformation with Numpy and SciPy_en.srt |
8.72KB |
| 002 Variable Transformation with Numpy and SciPy.mp4 |
42.45MB |
| 003 Bonus lecture.html |
625B |
| 003 Cardinality - categorical variables_en.srt |
6.38KB |
| 003 Cardinality - categorical variables.mp4 |
22.46MB |
| 003 Categorical variables_en.srt |
4.59KB |
| 003 Categorical variables.mp4 |
7.55MB |
| 003 Classification pipeline_en.srt |
16.56KB |
| 003 Classification pipeline.mp4 |
76.61MB |
| 003 Engineering time variables and different timezones_en.srt |
5.73KB |
| 003 Engineering time variables and different timezones.mp4 |
23.89MB |
| 003 How to approach this course.html |
1.69KB |
| 003 Important Feature-engine v 1.0.0.html |
739B |
| 003 Important Feature-engine version 1.0.0.html |
1009B |
| 003 KNN imputation - Demo_en.srt |
8.51KB |
| 003 KNN imputation - Demo.mp4 |
18.96MB |
| 003 Mean or median imputation_en.srt |
10.30KB |
| 003 Mean or median imputation.mp4 |
25.93MB |
| 003 Outlier capping with IQR_en.srt |
7.18KB |
| 003 Outlier capping with IQR.mp4 |
41.03MB |
| 003 Standardisation Demo_en.srt |
5.67KB |
| 003 Standardisation Demo.mp4 |
40.30MB |
| 003 Variable Transformation with Scikit-learn_en.srt |
8.01KB |
| 003 Variable Transformation with Scikit-learn.mp4 |
44.49MB |
| 004 Arbitrary value imputation_en.srt |
8.78KB |
| 004 Arbitrary value imputation.mp4 |
30.66MB |
| 004 Date and time variables_en.srt |
2.46KB |
| 004 Date and time variables.mp4 |
4.16MB |
| 004 Equal-width discretisation Demo_en.srt |
12.75KB |
| 004 Equal-width discretisation Demo.mp4 |
68.19MB |
| 004 Mean normalisation_en.srt |
5.04KB |
| 004 Mean normalisation.mp4 |
8.67MB |
| 004 MICE_en.srt |
8.50KB |
| 004 MICE.mp4 |
15.42MB |
| 004 One-hot-encoding Demo_en.srt |
18.05KB |
| 004 One-hot-encoding Demo.mp4 |
85.90MB |
| 004 Outlier capping with mean and std_en.srt |
5.17KB |
| 004 Outlier capping with mean and std.mp4 |
30.24MB |
| 004 Rare labels - categorical variables_en.srt |
6.23KB |
| 004 Rare labels - categorical variables.mp4 |
14.53MB |
| 004 Regression pipeline_en.srt |
17.46KB |
| 004 Regression pipeline.mp4 |
101.08MB |
| 004 Setting up your computer.html |
3.18KB |
| 004 Variable transformation with Feature-engine_en.srt |
4.37KB |
| 004 Variable transformation with Feature-engine.mp4 |
21.61MB |
| 005 Course material_en.srt |
2.28KB |
| 005 Course material.mp4 |
5.81MB |
| 005 End of distribution imputation_en.srt |
6.13KB |
| 005 End of distribution imputation.mp4 |
18.23MB |
| 005 Equal-frequency discretisation_en.srt |
4.88KB |
| 005 Equal-frequency discretisation.mp4 |
9.38MB |
| 005 Feature engineering pipeline with cross-validation_en.srt |
8.73KB |
| 005 Feature engineering pipeline with cross-validation.mp4 |
54.13MB |
| 005 Linear models assumptions_en.srt |
10.90KB |
| 005 Linear models assumptions.mp4 |
41.46MB |
| 005 Mean normalisation Demo_en.srt |
6.52KB |
| 005 Mean normalisation Demo.mp4 |
43.15MB |
| 005 missForest_en.srt |
1.26KB |
| 005 missForest.mp4 |
2.43MB |
| 005 Mixed variables_en.srt |
2.83KB |
| 005 Mixed variables.mp4 |
4.56MB |
| 005 One hot encoding of top categories_en.srt |
3.57KB |
| 005 One hot encoding of top categories.mp4 |
9.10MB |
| 005 Outlier capping with quantiles_en.srt |
3.83KB |
| 005 Outlier capping with quantiles.mp4 |
10.44MB |
| 005 sample-s2.csv |
9.94MB |
| 006 Arbitrary capping_en.srt |
3.99KB |
| 006 Arbitrary capping.mp4 |
15.08MB |
| 006 Download Jupyter notebooks.html |
1019B |
| 006 Equal-frequency discretisation Demo_en.srt |
7.99KB |
| 006 Equal-frequency discretisation Demo.mp4 |
40.99MB |
| 006 Frequent category imputation_en.srt |
8.60KB |
| 006 Frequent category imputation.mp4 |
38.09MB |
| 006 Linear model assumptions - additional reading resources (optional).html |
1.49KB |
| 006 MICE and missForest - Demo_en.srt |
5.17KB |
| 006 MICE and missForest - Demo.mp4 |
27.70MB |
| 006 More examples.html |
308B |
| 006 One hot encoding of top categories Demo_en.srt |
9.90KB |
| 006 One hot encoding of top categories Demo.mp4 |
53.90MB |
| 006 Scaling to minimum and maximum values_en.srt |
3.85KB |
| 006 Scaling to minimum and maximum values.mp4 |
7.48MB |
| 007 Additional reading resources (Optional).html |
1.15KB |
| 007 Download datasets.html |
3.46KB |
| 007 Important Feature-engine v1.0.0.html |
262B |
| 007 K-means discretisation_en.srt |
4.69KB |
| 007 K-means discretisation.mp4 |
8.42MB |
| 007 MinMaxScaling Demo_en.srt |
3.54KB |
| 007 MinMaxScaling Demo.mp4 |
24.91MB |
| 007 Missing category imputation_en.srt |
5.02KB |
| 007 Missing category imputation.mp4 |
23.41MB |
| 007 Ordinal encoding Label encoding_en.srt |
2.08KB |
| 007 Ordinal encoding Label encoding.mp4 |
4.87MB |
| 007 Variable distribution_en.srt |
6.46KB |
| 007 Variable distribution.mp4 |
14.93MB |
| 008 Additional reading resources.html |
526B |
| 008 Download presentations.html |
286B |
| 008 K-means discretisation Demo_en.srt |
3.19KB |
| 008 K-means discretisation Demo.mp4 |
16.24MB |
| 008 Maximum absolute scaling_en.srt |
3.36KB |
| 008 Maximum absolute scaling.mp4 |
6.53MB |
| 008 Ordinal encoding Demo_en.srt |
9.89KB |
| 008 Ordinal encoding Demo.mp4 |
49.50MB |
| 008 Outliers_en.srt |
10.67KB |
| 008 Outliers.mp4 |
18.65MB |
| 008 Random sample imputation_en.srt |
18.21KB |
| 008 Random sample imputation.mp4 |
87.64MB |
| 009 Adding a missing indicator_en.srt |
6.92KB |
| 009 Adding a missing indicator.mp4 |
14.68MB |
| 009 Count or frequency encoding_en.srt |
3.85KB |
| 009 Count or frequency encoding.mp4 |
6.87MB |
| 009 Discretisation plus categorical encoding_en.srt |
2.95KB |
| 009 Discretisation plus categorical encoding.mp4 |
5.91MB |
| 009 MaxAbsScaling Demo_en.srt |
4.57KB |
| 009 MaxAbsScaling Demo.mp4 |
27.14MB |
| 009 Moving forward_en.srt |
2.48KB |
| 009 Moving forward.mp4 |
3.91MB |
| 009 Variable magnitude_en.srt |
4.04KB |
| 009 Variable magnitude.mp4 |
7.41MB |
| 010 Count encoding Demo_en.srt |
5.32KB |
| 010 Count encoding Demo.mp4 |
16.65MB |
| 010 Discretisation plus encoding Demo_en.srt |
6.54KB |
| 010 Discretisation plus encoding Demo.mp4 |
33.97MB |
| 010 FAQ Data science, Python, datasets, presentations and more.html |
1.97KB |
| 010 Imputation with Scikit-learn_en.srt |
5.12KB |
| 010 Imputation with Scikit-learn.mp4 |
20.83MB |
| 010 ML-Comparison.pdf |
297.57KB |
| 010 Scaling to median and quantiles_en.srt |
3.24KB |
| 010 Scaling to median and quantiles.mp4 |
6.85MB |
| 010 Variable characteristics and machine learning models.html |
402B |
| 011 Additional reading resources.html |
4.51KB |
| 011 Discretisation with classification trees_en.srt |
5.80KB |
| 011 Discretisation with classification trees.mp4 |
20.37MB |
| 011 Mean or median imputation with Scikit-learn_en.srt |
6.52KB |
| 011 Mean or median imputation with Scikit-learn.mp4 |
37.92MB |
| 011 Robust Scaling Demo_en.srt |
2.44KB |
| 011 Robust Scaling Demo.mp4 |
15.83MB |
| 011 Target guided ordinal encoding_en.srt |
3.39KB |
| 011 Target guided ordinal encoding.mp4 |
7.02MB |
| 012 Arbitrary value imputation with Scikit-learn_en.srt |
6.39KB |
| 012 Arbitrary value imputation with Scikit-learn.mp4 |
36.35MB |
| 012 Discretisation with decision trees using Scikit-learn_en.srt |
13.74KB |
| 012 Discretisation with decision trees using Scikit-learn.mp4 |
75.56MB |
| 012 Scaling to vector unit length_en.srt |
6.80KB |
| 012 Scaling to vector unit length.mp4 |
13.07MB |
| 012 Target guided ordinal encoding Demo_en.srt |
9.77KB |
| 012 Target guided ordinal encoding Demo.mp4 |
65.87MB |
| 013 Discretisation with decision trees using Feature-engine_en.srt |
4.38KB |
| 013 Discretisation with decision trees using Feature-engine.mp4 |
24.84MB |
| 013 Frequent category imputation with Scikit-learn_en.srt |
6.73KB |
| 013 Frequent category imputation with Scikit-learn.mp4 |
35.30MB |
| 013 Mean encoding_en.srt |
2.92KB |
| 013 Mean encoding.mp4 |
5.20MB |
| 013 Scaling to vector unit length Demo_en.srt |
6.19KB |
| 013 Scaling to vector unit length Demo.mp4 |
44.81MB |
| 014 Additional reading resources.html |
1.34KB |
| 014 Domain knowledge discretisation_en.srt |
4.18KB |
| 014 Domain knowledge discretisation.mp4 |
18.93MB |
| 014 Mean encoding Demo_en.srt |
6.58KB |
| 014 Mean encoding Demo.mp4 |
36.23MB |
| 014 Missing category imputation with Scikit-learn_en.srt |
3.56KB |
| 014 Missing category imputation with Scikit-learn.mp4 |
19.97MB |
| 015 Adding a missing indicator with Scikit-learn_en.srt |
4.64KB |
| 015 Adding a missing indicator with Scikit-learn.mp4 |
23.27MB |
| 015 Additional reading resources.html |
1.41KB |
| 015 Probability ratio encoding_en.srt |
7.21KB |
| 015 Probability ratio encoding.mp4 |
22.57MB |
| 016 Automatic determination of imputation method with Sklearn_en.srt |
9.24KB |
| 016 Automatic determination of imputation method with Sklearn.mp4 |
65.39MB |
| 016 Weight of evidence (WoE)_en.srt |
6.43KB |
| 016 Weight of evidence (WoE).mp4 |
10.04MB |
| 017 Introduction to Feature-engine_en.srt |
8.34KB |
| 017 Introduction to Feature-engine.mp4 |
26.89MB |
| 017 Weight of Evidence Demo_en.srt |
16.69KB |
| 017 Weight of Evidence Demo.mp4 |
98.28MB |
| 018 Comparison of categorical variable encoding_en.srt |
13.36KB |
| 018 Comparison of categorical variable encoding.mp4 |
76.19MB |
| 018 Mean or median imputation with Feature-engine_en.srt |
5.49KB |
| 018 Mean or median imputation with Feature-engine.mp4 |
31.73MB |
| 019 Arbitrary value imputation with Feature-engine_en.srt |
3.78KB |
| 019 Arbitrary value imputation with Feature-engine.mp4 |
25.11MB |
| 019 Rare label encoding_en.srt |
5.18KB |
| 019 Rare label encoding.mp4 |
10.27MB |
| 020 End of distribution imputation with Feature-engine_en.srt |
5.81KB |
| 020 End of distribution imputation with Feature-engine.mp4 |
26.03MB |
| 020 Rare label encoding Demo_en.srt |
12.45KB |
| 020 Rare label encoding Demo.mp4 |
60.59MB |
| 021 Binary encoding and feature hashing_en.srt |
7.53KB |
| 021 Binary encoding and feature hashing.mp4 |
13.80MB |
| 021 Frequent category imputation with Feature-engine_en.srt |
1.98KB |
| 021 Frequent category imputation with Feature-engine.mp4 |
5.25MB |
| 022 Missing category imputation with Feature-engine_en.srt |
3.80KB |
| 022 Missing category imputation with Feature-engine.mp4 |
19.81MB |
| 022 Summary table of encoding techniques.html |
312B |
| 023 Additional reading resources.html |
2.37KB |
| 023 Random sample imputation with Feature-engine_en.srt |
2.86KB |
| 023 Random sample imputation with Feature-engine.mp4 |
16.88MB |
| 024 Adding a missing indicator with Feature-engine_en.srt |
4.88KB |
| 024 Adding a missing indicator with Feature-engine.mp4 |
28.02MB |
| 025 CCA with Feature-engine_en.srt |
8.46KB |
| 025 CCA with Feature-engine.mp4 |
37.30MB |
| 026 NA-methods-Comparison.pdf |
273.81KB |
| 026 Overview of missing value imputation methods.html |
339B |
| 027 Conclusion when to use each missing data imputation method.html |
2.69KB |
| Bonus Resources.txt |
386B |
| Get Bonus Downloads Here.url |
182B |
| loan.csv |
1.02MB |
| sample_s2.csv |
9.94MB |