# amueller/introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"

Repository: https://github.com/amueller/introduction_to_ml_with_python
Canonical: https://ross.abutalabs.com/products/introduction_to_ml_with_python
Language: Jupyter Notebook
License Family: other
Last push: 2024-03-14T02:46:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3748, "days_push": 902, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8161, forks 4670 (observed 2026-08-28T04:10:13.954944+00:00)

## What it is
Companion repository of Jupyter notebooks and the mglearn helper library for the O'Reilly book 'Introduction to Machine Learning with Python' by Andreas Mueller and Sarah Guido. It provides runnable examples, datasets, and figure-generation code built on scikit-learn, numpy, pandas, and matplotlib.

## Use cases
- learn machine learning with scikit-learn through notebooks
- follow along with the Introduction to Machine Learning with Python book
- find beginner-friendly ML code examples in Python
- get helper functions for plotting ML figures and datasets
- run ML tutorials interactively in Jupyter or Binder

## When to choose
- you are learning scikit-learn and want worked notebook examples
- you are reading the book and want the accompanying code and datasets
- you want simple illustrative plots of ML concepts like decision trees and cross-validation

## When to avoid
- you need a production-ready ML library or framework
- you need up-to-date examples for the latest scikit-learn versions
- you need a maintained tool with a license for redistribution

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, data-visualization
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, scikit-learn, book-companion, mglearn, educational

## Member repositories
- amueller/introduction_to_ml_with_python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:13.954944+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 2026-08-29T17:29:44.745646+00:00, confidence not recorded.
  - readme: https://github.com/amueller/introduction_to_ml_with_python (fetched 2026-08-28T04:10:13.954944+00:00, sha d58e0fca7be3)
- Data as of 2026-08-30T08:39:29.467469+00:00.