Python Machine Learning
Code notebooks for the 1st edition of the Python Machine Learning book by Sebastian Raschka.rasbt/python-machine-learning-book
- Stars
- 12.7k
- Forks
- 4.4k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 4 months ago
- Type
- Learning resource
What it is
This repository holds the Jupyter notebook code examples that accompany the 1st edition of the book Python Machine Learning (published 2015 by Packt). The book aims to explain machine learning concepts, best practices, and caveats, then applies them mainly with NumPy, scikit-learn, and Theano. The README notes that the notebooks are meant to be used alongside the book's formulae and text, not on their own.
- Open chapter notebooks to view the code examples in each book chapter
- Read the chapter notebooks through nbviewer without installing anything
- Use the equation reference PDF and TEX files for the book's math notation
- Follow the linked math and NumPy primers for background
Great for
- Readers who own the 1st edition and want to run its code
- Beginners who want theory paired with Python code
- Students working through introductory machine learning topics
- Instructors looking for chapter-by-chapter examples
Not ideal for
- Readers wanting the 2nd edition, since the README points to a separate repository for it
- Anyone wanting only standalone code without the book's explanations, since the README says the notebooks may not be useful without the text
- Readers who need current library versions, since the material dates from 2015 and uses Theano
How to get it
- 1
Clone and build from source
You'll need: Python (version not specified in the README excerpt) · Jupyter Notebook
Clone the repository
git clone https://github.com/rasbt/python-machine-learning-book.git - 2
Run it
- Clone the repository or open a chapter notebook on nbviewer
- Read the setup instructions in code/ch01/README.md
- Open a chapter notebook such as code/ch01/ch01.ipynb in Jupyter
- Run the cells and compare the output with the book text
- 3
Get the most out of it
- Read the chapter text in the book alongside each notebook, as the README recommends
- Use the equation reference PDF or TEX file for formulas
- Check the Google Group linked in the README for discussion
- Read the linked NumPy and math primers if the basics feel unfamiliar
Downloads
Strengths
- Free and MIT-licensed code that the author provides alongside a published book
- Chapter-by-chapter layout matches the book's table of contents
- Includes supporting material such as equation references, primers, and teaching slides
Trade-offs
- Code is from the 1st edition and the README does not state which library versions it targets
- Notebooks are not useful on their own without the book's explanations, according to the README
- Relies on Theano, which the README mentions but does not describe as current
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| python-machine-learning-book (this) | 12.7k | Learning resource | beginner | 4 months ago | Readers who own the 1st edition and want to run its code |
| ML-For-Beginners | 91.9k | Learning resource | beginner | 5 days ago | Beginners who want a structured, self-paced introduction to machine learning |
| ML-From-Scratch | 33.3k | Learning resource | beginner | 3.1 years ago | Students who want to see how algorithms like regression or neural networks work internally |
| 100-Days-Of-ML-Code | 52k | Learning resource | beginner | 2.9 years ago | Beginners who want a day-by-day sequence of basic machine learning topics |
| AI-Expert-Roadmap | 31.3k | Learning resource | beginner | 1.1 years ago | Beginners who are unsure what to learn next in AI or data science |
| spaCy | 34k | Library | intermediate | 10 days ago | Python developers building text-processing features into an application |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.