# jonkrohn/ML-foundations

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

Repository: https://github.com/jonkrohn/ML-foundations
Canonical: https://ross.abutalabs.com/products/ml-foundations
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, data-science, python, mathematics, linear-algebra, calculus, probability, statistics, computer-science, data-structures, numpy, pytorch, tensorflow, jupyter-notebook
Last push: 2024-11-20T19:39:24+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": 2328, "days_push": 651, "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 4849, forks 2314 (observed 2026-08-28T04:09:01.381918+00:00)

## What it is
A collection of Jupyter notebooks accompanying Jon Krohn's Machine Learning Foundations curriculum covering the math and computer science underpinnings of machine learning. It spans eight subjects across linear algebra, calculus, probability and statistics, and algorithms/optimization, with companion video playlists.

## Use cases
- learn the math foundations needed for machine learning
- study linear algebra for deep learning with Python examples
- review calculus and derivatives for gradient descent
- learn probability and statistics for data science
- practice algorithms and data structures relevant to ML
- follow a structured ML prerequisites curriculum with videos

## When to choose
- you want a structured, notebook-driven introduction to ML math
- you prefer learning with runnable Python/NumPy/PyTorch examples
- you want free video lectures paired with code

## When to avoid
- you need production ML tooling rather than educational material
- you already have strong math foundations and want advanced ML topics
- you need a maintained software library with an API

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science, math
- domain: machine-learning, data-science, mathematics, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, linear-algebra, calculus, statistics, computer-science, numpy, pytorch, tensorflow, curriculum, video-course

## Member repositories
- jonkrohn/ML-foundations (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:01.381918+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 2026-08-29T18:18:25.616348+00:00, confidence not recorded.
  - readme: https://github.com/jonkrohn/ML-foundations (fetched 2026-08-28T04:09:01.381918+00:00, sha 2deb95845ee2)
- Data as of 2026-08-30T08:39:29.467469+00:00.