# NabidAlam/road-to-machine-learning

A comprehensive, step-by-step guide to learning Machine Learning from absolute basics to advanced topics

Repository: https://github.com/NabidAlam/road-to-machine-learning
Canonical: https://ross.abutalabs.com/products/road-to-machine-learning
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-18T22:49:48+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 17
- inputs: {"age_days": 249, "days_push": 15, "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 1239, forks 402 (observed 2026-08-28T04:04:06.010154+00:00)

## What it is
An open-source, structured learning roadmap for machine learning, spanning 26 modules from absolute basics to advanced topics including deployment and MLOps. It includes 23 hands-on projects and career path guides for roles like data analyst and ML engineer.

## Use cases
- learn machine learning from scratch
- find a structured ML study roadmap
- build a machine learning project portfolio
- prepare for an ML engineer career switch
- learn MLOps and model deployment
- find beginner-friendly ML tutorials with projects

## When to choose
- you want a free, step-by-step curriculum from zero to advanced ML
- you need hands-on project ideas to build a portfolio
- you are a student or career switcher seeking a guided learning path

## When to avoid
- you need production ML tooling or code libraries rather than learning material
- you want a formal course with certification or mentorship
- you already need only advanced, specialized research-level ML resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, developer-tools
- domain: machine-learning, tutorials, data-science, education
- platform: python, cross-platform
- tags: roadmap, curriculum, mlops, career-guide, study-guide, beginner-friendly

## Member repositories
- NabidAlam/road-to-machine-learning (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:06.010154+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 2026-08-30T05:09:55.554972+00:00, confidence not recorded.
  - readme: https://github.com/NabidAlam/road-to-machine-learning (fetched 2026-08-28T04:04:06.010154+00:00, sha b9ab2dcee014)
- Data as of 2026-08-30T08:39:29.467469+00:00.