Udemy - Data pre-processing for Machine Learning in Python

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[ DevCourseWeb.com ] Udemy - Data pre-processing for Machine Learning in Python
  • 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)

    • Description

      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 pre-processing for Machine Learning in Python

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2 GB
seeders:7
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Udemy - Data pre-processing for Machine Learning in Python

Torrent hash: C3CAA5F1BEF84CDEF0DCD44E0DC80900FE02A8E6