# rasbt/python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

Repository: https://github.com/rasbt/python-machine-learning-book-2nd-edition
Canonical: https://ross.abutalabs.com/products/python-machine-learning-book-2nd-edition
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, python, scikit-learn, tensorflow, data-science
Last push: 2020-10-01T06:20:21+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": 3492, "days_push": 2162, "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 7201, forks 2790 (observed 2026-08-28T04:09:56.579680+00:00)

## What it is
The official code repository for the book 'Python Machine Learning (2nd edition)', containing Jupyter notebooks and Python scripts for each chapter. It covers machine learning and deep learning topics using scikit-learn and TensorFlow.

## Use cases
- learn machine learning with python from scratch
- find code examples for scikit-learn classifiers
- study deep learning with tensorflow notebooks
- follow along with a machine learning book chapter by chapter
- learn data preprocessing and model evaluation techniques
- understand dimensionality reduction with worked examples

## When to choose
- you are reading or own the 2nd edition of the book and want its companion code
- you prefer learning via stepwise-executable Jupyter notebooks
- you want MIT-licensed example code covering classic ML through TensorFlow

## When to avoid
- you want the latest content - the 3rd edition repository supersedes this one
- you need production-ready machine learning code rather than educational examples
- you expect the notebooks to be fully self-contained without the book's text

## 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- tags: jupyter-notebooks, scikit-learn, tensorflow, book-code, superseded

## Member repositories
- rasbt/python-machine-learning-book-2nd-edition (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:56.579680+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 2026-08-29T17:39:52.087101+00:00, confidence not recorded.
  - readme: https://github.com/rasbt/python-machine-learning-book-2nd-edition (fetched 2026-08-28T04:09:56.579680+00:00, sha af813cfb6c2f)
- Data as of 2026-08-30T08:39:29.467469+00:00.