# rasbt/python-machine-learning-book

The "Python Machine Learning (1st edition)"  book code repository and info resource

Repository: https://github.com/rasbt/python-machine-learning-book
Canonical: https://ross.abutalabs.com/products/python-machine-learning-book
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, machine-learning-algorithms, logistic-regression, data-science, data-mining, python, scikit-learn, neural-network
Last push: 2026-07-18T13:09:40+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 8, longevity 100
- inputs: {"age_days": 4044, "days_push": 46, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12644, forks 4373 (observed 2026-08-28T04:10:59.490016+00:00)

## What it is
The official code repository for the 1st edition of the book 'Python Machine Learning' by Sebastian Raschka, containing Jupyter Notebook code examples for each chapter. It covers machine learning theory and practice using NumPy, scikit-learn, and Theano.

## Use cases
- learn machine learning from scratch with python
- find code examples for scikit-learn algorithms
- understand neural network fundamentals with numpy
- study logistic regression and classification implementations
- get jupyter notebooks accompanying a machine learning book
- learn data preprocessing and model evaluation best practices

## When to choose
- you are reading the 1st edition of the Python Machine Learning book and want its code
- you want well-explained notebook-style walkthroughs of classic ML algorithms
- you prefer learning theory alongside runnable NumPy/scikit-learn examples

## When to avoid
- you want the latest edition's content - use the 2nd or 3rd edition repositories instead
- you need a production-ready ML library rather than educational code
- you rely on Theano, which is no longer maintained

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: jupyter-notebooks, scikit-learn, neural-networks, book-code, supervised-learning

## Member repositories
- rasbt/python-machine-learning-book (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:59.490016+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 2026-08-29T17:13:53.878356+00:00, confidence not recorded.
  - readme: https://github.com/rasbt/python-machine-learning-book (fetched 2026-08-28T04:10:59.490016+00:00, sha b8a7dd409542)
- Data as of 2026-08-30T08:39:29.467469+00:00.