# aburkov/theMLbook

The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.

Repository: https://github.com/aburkov/theMLbook
Canonical: https://ross.abutalabs.com/products/themlbook
Homepage: http://themlbook.com
Language: Python
License: MIT
License Family: permissive
Topics: python, machine-learning, machine-learning-algorithms, book
Last push: 2024-06-27T19:56:50+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": 2796, "days_push": 797, "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 2034, forks 590 (observed 2026-08-28T04:06:07.900163+00:00)

## What it is
Python code that reproduces the illustrations from Andriy Burkov's The Hundred-Page Machine Learning Book. It serves as a hands-on companion to the book, demonstrating core ML algorithms like SVMs, neural networks, ensembles, and clustering.

## Use cases
- learn machine learning by running code examples
- reproduce book illustrations in python
- understand how svm and neural network algorithms work
- study machine learning alongside the hundred-page book
- get example implementations of clustering and ensemble methods
- prepare for machine learning interviews with practical code

## When to choose
- you are reading The Hundred-Page Machine Learning Book and want runnable code
- you want concise, minimal implementations of classic ML algorithms
- you are a software engineer transitioning into machine learning

## When to avoid
- you need a production-ready machine learning library
- you want a comprehensive course or dataset rather than book companion code
- you need deep learning frameworks or GPU training pipelines

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python
- tags: book-companion-code, illustrations, supervised-learning, neural-networks, svm, clustering

## Member repositories
- aburkov/theMLbook (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:07.900163+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 2026-08-30T02:59:23.606179+00:00, confidence not recorded.
  - readme: https://github.com/aburkov/theMLbook (fetched 2026-08-28T04:06:07.900163+00:00, sha e7394bb0b44b)
  - homepage: http://themlbook.com (fetched 2026-08-29T10:39:11.143749+00:00, sha b35a1f011f52)
- Data as of 2026-08-30T08:39:29.467469+00:00.