Udemy - Data pre-processing for Machine Learning in Python

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1. Introduction
• 1. Introduction to the course.mp4 (17.5 MB)
• 1. Introduction to the course.srt (3.4 KB)
• 2. Numerical and categorical variables.mp4 (11.6 MB)
• 2. Numerical and categorical variables.srt (2.3 KB)
• 3. The dataset.html (0.4 KB)
• 3.1 sample_dataset_bins.csv (8.5 KB)
• 3.2 sample_dataset.csv (97.1 KB)
• 4. Required Python packages.html (0.9 KB)
• 5. Jupyter notebooks.mp4 (34.6 MB)
• 5. Jupyter notebooks.srt (9.4 KB)
10. Oversampling
• 1. Introduction to SMOTE.mp4 (19.6 MB)
• 1. Introduction to SMOTE.srt (5.1 KB)
• 2. How to perform SMOTE.mp4 (57.0 MB)
• 2. How to perform SMOTE.srt (10.1 KB)
• 2.1 How to do SMOTE.ipynb (8.7 KB)
• 3. Exercise.mp4 (35.5 MB)
• 3. Exercise.srt (5.6 KB)
• 3.1 Exercises.ipynb (4.9 KB)
11. General guidelines
• 1. Practical suggestions.html (1.4 KB)
2. Data cleaning
• 1. Introduction to data cleaning.mp4 (9.6 MB)
• 1. Introduction to data cleaning.srt (2.4 KB)
• 2. Selecting numerical and categorical variables.mp4 (27.6 MB)
• 2. Selecting numerical and categorical variables.srt (4.0 KB)
• 2.1 Select numerical and categorical variables.ipynb (4.5 KB)
• 3. Cleaning the numerical features.mp4 (59.1 MB)
• 3. Cleaning the numerical features.srt (10.5 KB)
• 3.1 Cleaning the numerical features.ipynb (7.6 KB)
• 4. Cleaning the categorical features.mp4 (17.0 MB)
• 4. Cleaning the categorical features.srt (3.7 KB)
• 4.1 Cleaning the categorical features.ipynb (34.2 KB)
• 5. KNN blank filling.mp4 (60.9 MB)
• 5. KNN blank filling.srt (10.6 KB)
• 5.1 Cleaning with KNN.ipynb (6.6 KB)
• 6. ColumnTransformer and make_column_selector.mp4 (88.4 MB)
• 6. ColumnTransformer and make_column_selector.srt (13.2 KB)
• 6.1 ColumnTransformer.ipynb (6.8 KB)
• 7. Exercises.mp4 (80.7 MB)
• 7. Exercises.srt (9.4 KB)
• 7.1 Exercises.ipynb (23.6 KB)
3. Encoding of the categorical features
• 1. Introduction to the encoding of categorical variables.mp4 (5.4 MB)
• 1. Introduction to the encoding of categorical variables.srt (1.3 KB)
• 2. One-hot encoding.mp4 (114.7 MB)
• 2. One-hot encoding.srt (19.8 KB)
• 2.1 One-hot encoding.ipynb (10.8 KB)
• 3. Ordinal encoding.mp4 (40.0 MB)
• 3. Ordinal encoding.srt (7.8 KB)
• 3.1 OrdinalEncoder.ipynb (3.6 KB)
• 4. Label encoding of the target variable.mp4 (10.1 MB)
• 4. Label encoding of the target variable.srt (2.4 KB)
• 4.1 LabelEncoder.ipynb (1.6 KB)
• 5. Exercise.mp4 (74.4 MB)
• 5. Exercise.srt (12.1 KB)
• 5.1 Exercises.ipynb (4.9 KB)
4. Transformations of the numerical features
• 1. Introduction to transformations.mp4 (10.8 MB)
• 1. Introduction to transformations.srt (2.6 KB)
• 2. Power Transformation.mp4 (48.7 MB)
• 2. Power Transformation.srt (8.7 KB)
• 2.1 Power Transform.ipynb (43.5 KB)
• 3. Binning.mp4 (60.4 MB)
• 3. Binning.srt (10.9 KB)
• 3.1 Binning.ipynb (30.3 KB)
• 4. Binarizing.mp4 (11.6 MB)
• 4. Binarizing.srt (2.4 KB)
• 4.1 Binarizer.ipynb (13.3 KB)
• 5. Applying an arbitrary transformation.mp4 (42.1 MB)
• 5. Applying an arbitrary transformation.srt (7.1 KB)
• 5.1 FunctionTransformer.ipynb (11.9 KB)
• 6. Exercise.mp4 (76.7 MB)
• 6. Exercise.srt (10.0 KB)
• 6.1 Exercises.ipynb (8.8 KB)
• 7. About power transformations.html (1.0 KB)
5. Pipelines
• 1. Define a transformation pipeline.mp4 (38.8 MB)
• 1. Define a transformation pipeline.srt (9.3 KB)
• 1.1 Define a transformation pipeline.ipynb (4.2 KB)
• 2. Pipelines and ColumnTransformer together.mp4 (78.6 MB)
• 2. Pipelines and ColumnTransformer together.srt (11.2 KB)
• 2.1 Pipelines and ColumnTransformer together .ipynb (5.5 KB)
• 3. Exercises.mp4 (78.7 MB)
• 3. Exercises.srt (10.6 KB)
• 3.1 Exercises.ipynb (6.2 KB)
6. Scaling
• 1. Introduction to scaling.mp4 (19.0 MB)
• 1. Introduction to scaling.srt (3.1 KB)
• 2. Normalization, Standardization, Robust scaling.mp4 (71.2 MB)
• 2. Normalization, Standardization, Robust scaling.srt (11.5 KB)
• 2.1 Scaling techniques.ipynb (14.2 KB)
• 3. Exercise.mp4 (50.6 MB)
• 3. Exercise.srt (6.5 KB)
• 3.1 Exercise.ipynb (4.5 KB)
7. Principal Component Analysis
• 1. Introduction to PCA.mp4 (18.8 MB)
• 1. Introduction to PCA.srt (4.0 KB)
• 2. How to perform PCA.mp4 (61.8 MB)
• 2. How to perform PCA.srt (8.6 KB)
• 2.1 PCA.ipynb (25.3 KB)
• 3. Exercise.mp4 (32.8 MB)
• 3. Exercise.srt (5.9 KB)
• 3.1 Exercises.ipynb (11.2 KB)
8. Filter-based feature selection
• 1. Introduction to feature selection.mp4 (28.6 MB)
• 1. Introduction to feature selection.srt (7.1 KB)
• 2. Numerical features, numerical target.mp4 (77.9 MB)
• 2. Numerical features, numerical target.srt (9.4 KB)
• 2.1 Numerical target numerical feature.ipynb (41.1 KB)
• 3. Numerical features, categorical target.mp4 (52.1 MB)
• 3. Numerical features, categorical target.srt (5.8 KB)
• 3.1 Numerical features categorical target.ipynb (13.0 KB)
• 4. Categorical features, numerical target.mp4 (71.1 MB)

• Code:

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