# INRIA/scikit-learn-mooc

Machine learning in Python with scikit-learn MOOC

Repository: https://github.com/INRIA/scikit-learn-mooc
Canonical: https://ross.abutalabs.com/products/scikit-learn-mooc
Homepage: https://inria.github.io/scikit-learn-mooc
Language: Jupyter Notebook
License: CC-BY-4.0
License Family: other
Topics: machine-learning, mooc, python, scikit-learn
Last push: 2026-08-14T14:31:52+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 8, longevity 100
- inputs: {"age_days": 2368, "days_push": 19, "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 1392, forks 600 (observed 2026-08-28T04:04:36.062048+00:00)

## What it is
The source material for a free MOOC teaching machine learning in Python with scikit-learn, developed by INRIA. It includes didactic lessons and executable Jupyter notebooks covering predictive modeling pipelines, preprocessing, model selection, and interpretation.

## Use cases
- learn machine learning with scikit-learn from scratch
- find a free beginner-friendly ML course in Python
- run hands-on scikit-learn notebooks locally or on Binder
- study predictive modeling pipeline design and model evaluation
- get course material for teaching scikit-learn workshops
- self-paced learning of data preprocessing and model interpretation

## When to choose
- you are a beginner wanting a structured, didactic introduction to scikit-learn
- you prefer executable notebooks and a self-paced MOOC format
- you need openly licensed (CC-BY) teaching material you can reuse or cite

## When to avoid
- you need deep learning or PyTorch/TensorFlow content
- you want a reference tool or library rather than course material
- you already have advanced scikit-learn expertise and need cutting-edge topics

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science
- domain: machine-learning, tutorials, data-science, education
- platform: python
- tags: scikit-learn, mooc, jupyter-notebooks, course-material, predictive-modeling, web-server

## Member repositories
- INRIA/scikit-learn-mooc (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.062048+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 2026-08-30T04:39:31.286575+00:00, confidence not recorded.
  - readme: https://github.com/INRIA/scikit-learn-mooc (fetched 2026-08-28T04:04:36.062048+00:00, sha 9f3ae54ca277)
  - homepage: https://inria.github.io/scikit-learn-mooc (fetched 2026-08-29T11:54:24.784762+00:00, sha 8449435834dd)
- Data as of 2026-08-30T08:39:29.467469+00:00.