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[ DevCourseWeb com ] Udemy - Data pre-processing for Machine Learning in Python
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1. Introduction
1. Introduction to the course.mp4
1. Introduction to the course.srt
2. Numerical and categorical variables.mp4
2. Numerical and categorical variables.srt
3. The dataset.html
3.1 sample_dataset_bins.csv
3.2 sample_dataset.csv
4. Required Python packages.html
5. Jupyter notebooks.mp4
5. Jupyter notebooks.srt
10. Oversampling
1. Introduction to SMOTE.mp4
1. Introduction to SMOTE.srt
2. How to perform SMOTE.mp4
2. How to perform SMOTE.srt
2.1 How to do SMOTE.ipynb
3. Exercise.mp4
3. Exercise.srt
3.1 Exercises.ipynb
11. General guidelines
1. Practical suggestions.html
2. Data cleaning
1. Introduction to data cleaning.mp4
1. Introduction to data cleaning.srt
2. Selecting numerical and categorical variables.mp4
2. Selecting numerical and categorical variables.srt
2.1 Select numerical and categorical variables.ipynb
3. Cleaning the numerical features.mp4
3. Cleaning the numerical features.srt
3.1 Cleaning the numerical features.ipynb
4. Cleaning the categorical features.mp4
4. Cleaning the categorical features.srt
4.1 Cleaning the categorical features.ipynb
5. KNN blank filling.mp4
5. KNN blank filling.srt
5.1 Cleaning with KNN.ipynb
6. ColumnTransformer and make_column_selector.mp4
6. ColumnTransformer and make_column_selector.srt
6.1 ColumnTransformer.ipynb
7. Exercises.mp4
7. Exercises.srt
7.1 Exercises.ipynb
3. Encoding of the categorical features
1. Introduction to the encoding of categorical variables.mp4
1. Introduction to the encoding of categorical variables.srt
2. One-hot encoding.mp4
2. One-hot encoding.srt
2.1 One-hot encoding.ipynb
3. Ordinal encoding.mp4
3. Ordinal encoding.srt
3.1 OrdinalEncoder.ipynb
4. Label encoding of the target variable.mp4
4. Label encoding of the target variable.srt
4.1 LabelEncoder.ipynb
5. Exercise.mp4
5. Exercise.srt
5.1 Exercises.ipynb
4. Transformations of the numerical features
1. Introduction to transformations.mp4
1. Introduction to transformations.srt
2. Power Transformation.mp4
2. Power Transformation.srt
2.1 Power Transform.ipynb
3. Binning.mp4
3. Binning.srt
3.1 Binning.ipynb
4. Binarizing.mp4
4. Binarizing.srt
4.1 Binarizer.ipynb
5. Applying an arbitrary transformation.mp4
5. Applying an arbitrary transformation.srt
5.1 FunctionTransformer.ipynb
6. Exercise.mp4
6. Exercise.srt
6.1 Exercises.ipynb
7. About power transformations.html
5. Pipelines
1. Define a transformation pipeline.mp4
1. Define a transformation pipeline.srt
1.1 Define a transformation pipeline.ipynb
2. Pipelines and ColumnTransformer together.mp4
2. Pipelines and ColumnTransformer together.srt
2.1 Pipelines and ColumnTransformer together .ipynb
3. Exercises.mp4
3. Exercises.srt
3.1 Exercises.ipynb
6. Scaling
1. Introduction to scaling.mp4
1. Introduction to scaling.srt
2. Normalization, Standardization, Robust scaling.mp4
2. Normalization, Standardization, Robust scaling.srt
2.1 Scaling techniques.ipynb
3. Exercise.mp4
3. Exercise.srt
3.1 Exercise.ipynb
7. Principal Component Analysis
1. Introduction to PCA.mp4
1. Introduction to PCA.srt
2. How to perform PCA.mp4
2. How to perform PCA.srt
2.1 PCA.ipynb
3. Exercise.mp4
3. Exercise.srt
3.1 Exercises.ipynb
8. Filter-based feature selection
1. Introduction to feature selection.mp4
1. Introduction to feature selection.srt
2. Numerical features, numerical target.mp4
2. Numerical features, numerical target.srt
2.1 Numerical target numerical feature.ipynb
3. Numerical features, categorical target.mp4
3. Numerical features, categorical target.srt
3.1 Numerical features categorical target.ipynb
4. Categorical features, numerical target.mp4
4. Categorical features, numerical target.srt
4.1 Categorical features numerical target.ipynb
5. Categorical features, categorical target.mp4
5. Categorical features, categorical target.srt
5.1 Categorical features categorical target.ipynb
6. Feature importance according to a model.mp4
6. Feature importance according to a model.srt
6.1 Feature importance according to model.ipynb
7. A comment on mutual information.html
8. A comment on feature selection with categorical variables.html
9. Exercises.mp4
9. Exercises.srt
9.1 Exercises.ipynb
9. A complete pipeline
1. An example of a complete pipeline.mp4
1. An example of a complete pipeline.srt
1.1 A complete pipeline.ipynb
Bonus Resources.txt
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