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