#3838th of 41 in Learning and cookbooks
python-machine-learning-book
Jupyter notebooks for the first edition of Python Machine Learning
- Stars
- 12.7k
- License
- MIT
- Last commit
- Jul 2026
- Last release
- Sep 2016
- Language
- Jupyter Notebook
12,518 on 31 Mar 202612,659 on 20 Oct 2026
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Checked on GitHub.
Overview
Code companion to the 1st edition (2015) of Sebastian Raschka's Python Machine Learning book. It provides notebooks for 13 chapters covering classification, preprocessing, dimensionality reduction, ensembles, sentiment analysis, regression, clustering and neural networks, using NumPy, scikit-learn and Theano. Bonus notebooks and an equation reference are included; 2nd edition code is in a separate repository.
Who it is for: Readers of the book learning classic ML with scikit-learn
Strengths
- 13 chapter notebooks plus bonus ones such as nested cross-validation and softmax regression
- Includes an equation reference (PDF and TEX) and a barebones Flask web app example
- MIT licensed, with errata and reference lists in docs
Weaknesses
- Covers only the 1st edition; the README points to a separate 2nd edition repository
- Neural network chapters use Theano
- Notebooks are of limited use without the book's text and formulae
- Last release is from 2016-09-30; the README lists no pinned dependency versions
What it needs
- GPU optional
- Needs Python, Jupyter Notebook, NumPy, scikit-learn, Theano, Flask
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