# dafriedman97/mlbook

Repository for the free online book Machine Learning from Scratch (link below!)

Repository: https://github.com/dafriedman97/mlbook
Canonical: https://ross.abutalabs.com/products/mlbook
Homepage: https://dafriedman97.github.io/mlbook/content/introduction.html
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2023-08-30T22:02:54+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": 2274, "days_push": 1099, "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 1296, forks 226 (observed 2026-08-28T04:04:16.956065+00:00)

## What it is
A free online book, 'Machine Learning from Scratch', that derives common machine learning methods mathematically and implements them in Python from scratch. Each chapter includes concept, construction, and implementation sections using libraries like scikit-learn, statsmodels, and TensorFlow.

## Use cases
- learn machine learning algorithms from scratch
- understand the math behind common ML methods
- implement ML algorithms in Python without libraries
- find a free introductory machine learning book
- see how scikit-learn and tensorflow models work under the hood

## When to choose
- you want mathematical derivations paired with from-scratch Python implementations
- you prefer a free, concise alternative to ISL or ESL
- you learn best by building algorithms yourself

## When to avoid
- you need comprehensive coverage of modern deep learning topics
- you want production-ready ML code rather than educational examples
- you need a polished PDF or print edition

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python
- tags: free-book, jupyter-notebook, algorithms-from-scratch, scikit-learn, tensorflow

## Member repositories
- dafriedman97/mlbook (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:16.956065+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 2026-08-30T04:53:56.894604+00:00, confidence not recorded.
  - readme: https://github.com/dafriedman97/mlbook (fetched 2026-08-28T04:04:16.956065+00:00, sha cd7e5e51c969)
  - homepage: https://dafriedman97.github.io/mlbook/content/introduction.html (fetched 2026-08-29T12:10:32.189678+00:00, sha 8144bb461ff7)
- Data as of 2026-08-30T08:39:29.467469+00:00.