# jakevdp/sklearn_tutorial

Materials for my scikit-learn tutorial

Repository: https://github.com/jakevdp/sklearn_tutorial
Canonical: https://ross.abutalabs.com/products/sklearn_tutorial
Language: Jupyter Notebook
License: BSD-3-Clause
License Family: permissive
Archived: true
Last push: 2023-03-23T14:14:32+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4012, "days_push": 1259, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1831, forks 960 (observed 2026-08-28T04:05:41.903311+00:00)

## What it is
A collection of Jupyter notebooks and supporting materials for Jake VanderPlas's scikit-learn tutorial. It covers machine learning fundamentals using scikit-learn, numpy, scipy, matplotlib, and seaborn.

## Use cases
- learn scikit-learn from scratch
- find a hands-on machine learning tutorial in python
- study supervised and unsupervised learning examples
- get jupyter notebooks demonstrating sklearn workflows
- prepare for teaching a scikit-learn workshop

## When to choose
- you want a well-regarded, notebook-based introduction to scikit-learn
- you prefer learning through runnable examples with matplotlib/seaborn visualizations
- you are comfortable with python and want free tutorial material

## When to avoid
- you need up-to-date coverage of the latest scikit-learn APIs, as the material dates from 2015-2018
- you want a maintained library or production tool rather than educational notebooks
- you need deep-learning or LLM topics, which are not covered

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, developer-tools
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: scikit-learn, jupyter-notebooks, tutorial-materials, supervised-learning, unsupervised-learning

## Member repositories
- jakevdp/sklearn_tutorial (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:41.903311+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 2026-08-30T03:19:12.029506+00:00, confidence not recorded.
  - readme: https://github.com/jakevdp/sklearn_tutorial (fetched 2026-08-28T04:05:41.903311+00:00, sha 45ecdcfc2dc8)
- Data as of 2026-08-30T08:39:29.467469+00:00.