# AssemblyAI-Community/ML-Study-Guide

Minimal Machine Learning Study Plan

Repository: https://github.com/AssemblyAI-Community/ML-Study-Guide
Canonical: https://ross.abutalabs.com/products/ml-study-guide
License Family: other
Last push: 2024-06-13T05:49:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1534, "days_push": 811, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1852, forks 369 (observed 2026-08-28T04:05:44.455549+00:00)

## What it is
A curated machine learning study guide outlining a step-by-step path from math and Python basics through ML courses, Kaggle practice, and specialization. It is a collection of links to courses, videos, and books rather than software.

## Use cases
- find a roadmap to learn machine learning from scratch
- figure out what math to study before ML
- get recommendations for beginner ML courses and books
- plan a self-taught path into a machine learning career
- find Kaggle practice resources after learning ML basics

## When to choose
- you want a structured, opinionated learning path for machine learning
- you are a beginner deciding what to learn first (math, Python, ML stack)
- you prefer curated links to videos, courses, and books over building your own curriculum

## When to avoid
- you need runnable code, a library, or a tool rather than a study plan
- you want an in-depth, comprehensive ML curriculum with exercises and grading
- you need up-to-date content, as the guide is a static list of links

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: study-guide, machine-learning, curriculum, self-learning, awesome-lists

## Member repositories
- AssemblyAI-Community/ML-Study-Guide (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:44.455549+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 2026-08-30T03:16:51.499455+00:00, confidence not recorded.
  - readme: https://github.com/AssemblyAI-Community/ML-Study-Guide (fetched 2026-08-28T04:05:44.455549+00:00, sha cd914d8a541e)
- Data as of 2026-08-30T08:39:29.467469+00:00.