# rasbt/python-machine-learning-book-3rd-edition

The "Python Machine Learning (3rd edition)" book code repository

Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
Canonical: https://ross.abutalabs.com/products/python-machine-learning-book-3rd-edition
Homepage: https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, tensorflow, scikit-learn
Last push: 2023-04-19T08:03:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2644, "days_push": 1232, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5050, forks 2059 (observed 2026-08-28T04:09:09.027927+00:00)

## What it is
The official code repository for the book 'Python Machine Learning, 3rd Edition' by Sebastian Raschka and Vahid Mirjalili, containing Jupyter notebooks for all 18 chapters. It covers machine learning fundamentals with scikit-learn through deep learning with TensorFlow, including CNNs, RNNs, GANs, and reinforcement learning.

## Use cases
- learn machine learning with python from scratch
- study deep learning with tensorflow notebooks
- understand scikit-learn classifiers with worked examples
- learn how neural networks work by implementing them from scratch
- find example code for GANs and reinforcement learning
- supplement a machine learning textbook with runnable code

## When to choose
- you are reading or studying Python Machine Learning 3rd Edition
- you want structured, chapter-by-chapter ML learning notebooks
- you prefer learning concepts alongside runnable Jupyter code

## When to avoid
- you need a production-ready ML library rather than educational code
- you want standalone tutorials without the accompanying book text
- you need up-to-date examples for the latest TensorFlow versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, data-science, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, book-code, scikit-learn, tensorflow, education

## Member repositories
- rasbt/python-machine-learning-book-3rd-edition (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.027927+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:17:21.638927+00:00, confidence not recorded.
  - readme: https://github.com/rasbt/python-machine-learning-book-3rd-edition (fetched 2026-08-28T04:09:09.027927+00:00, sha b901783d90e3)
  - homepage: https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/ (fetched 2026-08-29T08:57:49.197081+00:00, sha 6d1d7ac1af17)
- Data as of 2026-08-30T08:39:29.467469+00:00.