# microsoft/ML-For-Beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Repository: https://github.com/microsoft/ML-For-Beginners
Canonical: https://ross.abutalabs.com/products/ml-for-beginners
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: ml, data-science, machine-learning, machine-learning-algorithms, machinelearning, python, machinelearning-python, scikit-learn, scikit-learn-python, r, education, microsoft-for-beginners
Last push: 2026-08-20T01:11:33+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 2010, "days_push": 14, "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 89825, forks 22093 (observed 2026-08-28T04:12:23.192407+00:00)

## What it is
A 12-week, 26-lesson open-source curriculum from Microsoft teaching classic machine learning with Python and scikit-learn, including 52 quizzes and hands-on Jupyter notebooks. It is available in many languages via automated translation.

## Use cases
- learn machine learning from scratch
- find a structured ML curriculum for self-study
- teach an introductory machine learning course
- practice ML with scikit-learn notebooks
- prepare for a data science career with guided lessons
- supplement a university ML class with exercises and quizzes

## When to choose
- you are a beginner wanting a free, structured, project-based ML course
- you need classroom-ready lessons with quizzes and assignments
- you prefer learning classic ML with scikit-learn in Python or R

## When to avoid
- you need deep learning, transformers, or LLM-focused content
- you want production ML engineering or MLOps guidance
- you need a software library or tool rather than educational material

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: curriculum, scikit-learn, beginners, jupyter-notebooks, quizzes, microsoft

## Member repositories
- microsoft/ML-For-Beginners (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:23.192407+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 2026-08-29T16:13:52.016879+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/ML-For-Beginners (fetched 2026-08-28T04:12:23.192407+00:00, sha 7439baa6b7fc)
- Data as of 2026-08-30T08:39:29.467469+00:00.