These are my notes and workbooks created while working my way through the Python Machine Learning book.

If you are looking for the the author's official repository with the original source code, check https://github.com/rasbt/python-machine-learning-book-3rd-edition

• Chapter 2 - Training simple ML algorithms for classification
• Chapter 3 - A tour of machine learning classifiers using scikit-learn
• Chapter 4 - Building good training datasets - Data processing
• Chapter 5 - Compressing data via dimensionality reduction
• Chapter 6 - Best practices for model evaluation and hyperparameter tuning
• Chapter 7 - Combining different models for ensemble learning
• Chapter 12 - Implementing a multilayer artificial neural network from scratch
• Chapter 13 - Parallelizing neural network training with TensorFlow
• Chapter 14 - Going deeper - The mechanics of tensorflow