# MicrosoftDocs/ml-basics

Exercise notebooks for Machine Learning modules on Microsoft Learn

Repository: https://github.com/MicrosoftDocs/ml-basics
Canonical: https://ross.abutalabs.com/products/ml-basics
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2025-08-12T15:39:23+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 36, release rhythm 35, longevity 100
- inputs: {"age_days": 2272, "days_push": 386, "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 1936, forks 2330 (observed 2026-08-28T04:05:57.069764+00:00)

## What it is
A collection of Jupyter Notebook exercise files accompanying the 'Create machine learning models' learning path on Microsoft Learn. It provides hands-on notebooks for learning machine learning fundamentals.

## Use cases
- learn machine learning basics through hands-on notebooks
- practice exercises for the Microsoft Learn ML path
- teach introductory machine learning with Jupyter notebooks
- work through supervised learning examples in Python
- supplement an ML course with guided exercises

## When to choose
- you are following the Microsoft Learn 'Create machine learning models' path
- you want beginner-friendly ML exercises in Jupyter notebooks
- you need ready-made teaching materials for intro ML

## When to avoid
- you need production machine learning code or a library
- you want advanced or research-level ML content
- you expect community contributions or frequent feature updates

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, microsoft-learn, exercises, beginner-friendly

## Member repositories
- MicrosoftDocs/ml-basics (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:57.069764+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 2026-08-30T03:08:25.398222+00:00, confidence not recorded.
  - readme: https://github.com/MicrosoftDocs/ml-basics (fetched 2026-08-28T04:05:57.069764+00:00, sha 16fe6d68d738)
- Data as of 2026-08-30T08:39:29.467469+00:00.