Master Machine Learning with Python provides a comprehensive guide to become an expert at both the concepts and tools of building a machine learning workflow using Python. At present, it includes material on the following:
- What is a Machine Learning Model?
- Linear Regression
- The three-step learning process with scikit-learn
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Model Evaluation
- Model Selection
- Missing Value Imputation
- Handling Categorical Data
- Building Pipelines
- Model Persistence
This book is a work in progress and is receiving updates regularly. It will continue to receive major updates through the year 2020. This current offer includes a PDF file of the book and solutions, all Jupyter Notebooks and videos of each chapter. Note that much of the content has yet to be produced. No videos have been produced at this time either.
If you wish to purchase the book without the videos, visit the book-only checkout.
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