| 1. An example of a complete pipeline.mp4 | 121.2 MB | ||
| 1. An example of a complete pipeline.srt | 17.9 KB | ||
| 1. Define a transformation pipeline.mp4 | 38.8 MB | ||
| 1. Define a transformation pipeline.srt | 9.3 KB | ||
| 1. Introduction to PCA.mp4 | 18.8 MB | ||
| 1. Introduction to PCA.srt | 4 KB | ||
| 1. Introduction to SMOTE.mp4 | 19.6 MB | ||
| 1. Introduction to SMOTE.srt | 5.1 KB | ||
| 1. Introduction to data cleaning.mp4 | 9.6 MB | ||
| 1. Introduction to data cleaning.srt | 2.4 KB | ||
| 1. Introduction to feature selection.mp4 | 28.6 MB | ||
| 1. Introduction to feature selection.srt | 7.1 KB | ||
| 1. Introduction to scaling.mp4 | 19 MB | ||
| 1. Introduction to scaling.srt | 3.1 KB | ||
| 1. Introduction to the course.mp4 | 17.5 MB | ||
| 1. Introduction to the course.srt | 3.4 KB | ||
| 1. Introduction to the encoding of categorical variables.mp4 | 5.4 MB | ||
| 1. Introduction to the encoding of categorical variables.srt | 1.3 KB | ||
| 1. Introduction to transformations.mp4 | 10.8 MB | ||
| 1. Introduction to transformations.srt | 2.6 KB | ||
| 1. Practical suggestions.html | 1.4 KB | ||
| 1.1 A complete pipeline.ipynb | 11 KB | ||
| 1.1 Define a transformation pipeline.ipynb | 4.2 KB | ||
| 2. How to perform PCA.mp4 | 61.8 MB | ||
| 2. How to perform PCA.srt | 8.6 KB | ||
| 2. How to perform SMOTE.mp4 | 57 MB | ||
| 2. How to perform SMOTE.srt | 10.1 KB | ||
| 2. Normalization, Standardization, Robust scaling.mp4 | 71.2 MB | ||
| 2. Normalization, Standardization, Robust scaling.srt | 11.5 KB | ||
| 2. Numerical and categorical variables.mp4 | 11.6 MB | ||
| 2. Numerical and categorical variables.srt | 2.3 KB | ||
| 2. Numerical features, numerical target.mp4 | 77.9 MB | ||
| 2. Numerical features, numerical target.srt | 9.4 KB | ||
| 2. One-hot encoding.mp4 | 114.7 MB | ||
| 2. One-hot encoding.srt | 19.8 KB | ||
| 2. Pipelines and ColumnTransformer together.mp4 | 78.6 MB | ||
| 2. Pipelines and ColumnTransformer together.srt | 11.2 KB | ||
| 2. Power Transformation.mp4 | 48.7 MB | ||
| 2. Power Transformation.srt | 8.7 KB | ||
| 2. Selecting numerical and categorical variables.mp4 | 27.6 MB | ||
| 2. Selecting numerical and categorical variables.srt | 4 KB | ||
| 2.1 How to do SMOTE.ipynb | 8.7 KB | ||
| 2.1 Numerical target numerical feature.ipynb | 41.1 KB | ||
| 2.1 One-hot encoding.ipynb | 10.8 KB | ||
| 2.1 PCA.ipynb | 25.3 KB | ||
| 2.1 Pipelines and ColumnTransformer together .ipynb | 5.5 KB | ||
| 2.1 Power Transform.ipynb | 43.5 KB | ||
| 2.1 Scaling techniques.ipynb | 14.2 KB | ||
| 2.1 Select numerical and categorical variables.ipynb | 4.5 KB | ||
| 3. Binning.mp4 | 60.4 MB | ||
| 3. Binning.srt | 10.9 KB | ||
| 3. Cleaning the numerical features.mp4 | 59.1 MB | ||
| 3. Cleaning the numerical features.srt | 10.5 KB | ||
| 3. Exercise.mp4 | 32.8 MB | ||
| 3. Exercise.srt | 5.9 KB | ||
| 3. Exercises.mp4 | 78.7 MB | ||
| 3. Exercises.srt | 10.6 KB | ||
| 3. Numerical features, categorical target.mp4 | 52.1 MB | ||
| 3. Numerical features, categorical target.srt | 5.8 KB | ||
| 3. Ordinal encoding.mp4 | 40 MB | ||
| 3. Ordinal encoding.srt | 7.8 KB | ||
| 3. The dataset.html | 409.6 B | ||
| 3.1 Binning.ipynb | 30.3 KB | ||
| 3.1 Cleaning the numerical features.ipynb | 7.6 KB | ||
| 3.1 Exercise.ipynb | 4.5 KB | ||
| 3.1 Exercises.ipynb | 11.2 KB | ||
| 3.1 Numerical features categorical target.ipynb | 13 KB | ||
| 3.1 OrdinalEncoder.ipynb | 3.6 KB | ||
| 3.1 sample_dataset_bins.csv | 8.5 KB | ||
| 3.2 sample_dataset.csv | 97.1 KB | ||
| 4. Binarizing.mp4 | 11.6 MB | ||
| 4. Binarizing.srt | 2.4 KB | ||
| 4. Categorical features, numerical target.mp4 | 71.1 MB | ||
| 4. Categorical features, numerical target.srt | 9.2 KB | ||
| 4. Cleaning the categorical features.mp4 | 17 MB | ||
| 4. Cleaning the categorical features.srt | 3.7 KB | ||
| 4. Label encoding of the target variable.mp4 | 10.1 MB | ||
| 4. Label encoding of the target variable.srt | 2.4 KB | ||
| 4. Required Python packages.html | 921.6 B | ||
| 4.1 Binarizer.ipynb | 13.3 KB | ||
| 4.1 Categorical features numerical target.ipynb | 44.5 KB | ||
| 4.1 Cleaning the categorical features.ipynb | 34.2 KB | ||
| 4.1 LabelEncoder.ipynb | 1.6 KB | ||
| 5. Applying an arbitrary transformation.mp4 | 42.1 MB | ||
| 5. Applying an arbitrary transformation.srt | 7.1 KB | ||
| 5. Categorical features, categorical target.mp4 | 56.9 MB | ||
| 5. Categorical features, categorical target.srt | 6.8 KB | ||
| 5. Exercise.mp4 | 74.4 MB | ||
| 5. Exercise.srt | 12.1 KB | ||
| 5. Jupyter notebooks.mp4 | 34.6 MB | ||
| 5. Jupyter notebooks.srt | 9.4 KB | ||
| 5. KNN blank filling.mp4 | 60.9 MB | ||
| 5. KNN blank filling.srt | 10.6 KB | ||
| 5.1 Categorical features categorical target.ipynb | 43.1 KB | ||
| 5.1 Cleaning with KNN.ipynb | 6.6 KB | ||
| 5.1 Exercises.ipynb | 4.9 KB | ||
| 5.1 FunctionTransformer.ipynb | 11.9 KB | ||
| 6. ColumnTransformer and make_column_selector.mp4 | 88.4 MB | ||
| 6. ColumnTransformer and make_column_selector.srt | 13.2 KB | ||
| 6. Exercise.mp4 | 76.7 MB | ||
| 6. Exercise.srt | 10 KB | ||
| 6. Feature importance according to a model.mp4 | 87.4 MB | ||
| 6. Feature importance according to a model.srt | 10.8 KB | ||
| 6.1 ColumnTransformer.ipynb | 6.8 KB | ||
| 6.1 Exercises.ipynb | 8.8 KB | ||
| 6.1 Feature importance according to model.ipynb | 26.2 KB | ||
| 7. A comment on mutual information.html | 1.1 KB | ||
| 7. About power transformations.html | 1 KB | ||
| 7. Exercises.mp4 | 80.7 MB | ||
| 7. Exercises.srt | 9.4 KB | ||
| 7.1 Exercises.ipynb | 23.6 KB | ||
| 8. A comment on feature selection with categorical variables.html | 1 KB | ||
| 9. Exercises.mp4 | 53.8 MB | ||
| 9. Exercises.srt | 8.4 KB | ||
| 9.1 Exercises.ipynb | 4.9 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 123 total files | |||
Data pre-processing for Machine Learning in Python
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 47 lectures (5h 35m) | Size: 2 GB
How to transform a dataset for a machine learning model
What you'll learn
How to fill the missings in numerical and categorical variables
How to encode the categorical variables
How to transform the numerical variables
How to scale the numerical variables
Principal Component Analysis and how to use it
How to apply oversampling using SMOTE
How to use several useful objects in scikit-learn library
Requirements
Basic knowledge of Python programming language
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Udemy - Data Analytics for Beginners - Excel, SQL, and AI Dashboards Posted by
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| 2.5 GB | freecoursewb | 1 day | 56 | 10 |
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