# ajaymache/machine-learning-yearning

Machine Learning Yearning book by  🅰️𝓷𝓭𝓻𝓮𝔀 🆖

Repository: https://github.com/ajaymache/machine-learning-yearning
Canonical: https://ross.abutalabs.com/products/ajaymache-machine-learning-yearning
License: MIT
License Family: permissive
Topics: machine-learning, machine-learning-yearning, andrew-ng-machine-learning, andrew-ng-machine-learning-yearning, deep-learning, deep-learning-andrew-ng, deeplearning-ai, machine-learning-coursera
Last push: 2018-12-20T02:08:09+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": 3060, "days_push": 2814, "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 1148, forks 310 (observed 2026-08-28T04:03:46.029928+00:00)

## What it is
A repository hosting 'Machine Learning Yearning', Andrew Ng's book on technical strategy for AI engineers in the deep learning era, distributed as PDFs split into 13 parts plus a consolidated full book. It covers how to align ML strategies in teams and how to set up dev/test sets for modern ML projects.

## Use cases
- learn machine learning project strategy from Andrew Ng
- how to set up dev and test sets for ML projects
- free machine learning book pdf download
- understand how to prioritize ML improvements in a team
- deep learning strategy guidance for AI engineers
- supplement to Andrew Ng's Coursera machine learning course

## When to choose
- you want conceptual guidance on structuring and iterating on ML projects
- you prefer reading a curated book over scattered tutorials
- you need offline PDF access to the full text

## When to avoid
- you need hands-on code examples or runnable notebooks
- you want up-to-date content - the book dates from 2018
- you need a software tool rather than reading material

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, machine-learning
- domain: machine-learning, deep-learning, tutorials, artificial-intelligence
- platform: cross-platform
- tags: book, andrew-ng, pdf, machine-learning-strategy, educational-resource

## Member repositories
- ajaymache/machine-learning-yearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:46.029928+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 2026-08-30T06:33:48.850074+00:00, confidence not recorded.
  - readme: https://github.com/ajaymache/machine-learning-yearning (fetched 2026-08-28T04:03:46.029928+00:00, sha 634d1dab222d)
- Data as of 2026-08-30T08:39:29.467469+00:00.