python-machine-learning-book-2nd-edition
The "Python Machine Learning (2nd edition)" book code repository and info resource
This repo is the companion code for the book "Python Machine Learning" second edition, containing 16 Jupyter Notebook chapters on machine learning and deep learning using scikit-learn and TensorFlow.
This repository contains the code that accompanies the second edition of the book "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili, published by Packt in September 2017. The book is a 622-page guide to building machine learning models using Python. The repository is a companion resource, not a standalone learning tool; the author notes that the notebooks may not be useful without the explanatory text and formulas from the book itself.
The code is organized into 16 chapters, each stored as a Jupyter Notebook. Jupyter Notebooks are interactive documents that mix code, explanations, and output side by side, which makes them well-suited for educational material. The chapters cover a wide range of topics, starting with basic classification algorithms and working up through neural networks, image recognition, and sequence modeling. There is also a chapter on embedding a machine learning model into a web application.
The tools used throughout the book include scikit-learn, a popular Python library for standard machine learning tasks, and TensorFlow, a framework from Google for building and training neural networks. The second edition was a significant update from the first: the deep learning chapters were reworked to use TensorFlow instead of the older Theano library, three new chapters on deep learning were added, and the figures and plots were redesigned. The author also addressed reader feedback about unclear explanations and corrected errors from the first edition.
A third edition was published in December 2019, and the author links to that repository from this one. This second-edition repository remains available for readers of that version. Translations in German and Japanese were also published.
Installation instructions for the required software are included in the first chapter's folder. The notebooks are intended to be worked through in order, following the book.
Where it fits
- Follow along with the Python Machine Learning second edition book chapter by chapter using runnable code notebooks.
- Study how to build and train neural networks with TensorFlow through the worked examples in the deep learning chapters.
- Practice classification, image recognition, and sequence modeling using the scikit-learn examples in the notebooks.