# kaieye/2022-Machine-Learning-Specialization

Repository: https://github.com/kaieye/2022-Machine-Learning-Specialization
Canonical: https://ross.abutalabs.com/products/2022-machine-learning-specialization
Language: Jupyter Notebook
License Family: other
Last push: 2024-08-26T03:31:01+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": 1539, "days_push": 737, "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 4609, forks 1625 (observed 2026-08-28T04:08:54.960019+00:00)

## What it is
A community-maintained collection of Jupyter Notebook labs, quizzes, and markdown notes for Andrew Ng's 2022 Machine Learning Specialization from Coursera. It covers supervised learning, advanced learning algorithms, unsupervised learning, recommenders, and reinforcement learning.

## Use cases
- follow along with Andrew Ng's machine learning specialization labs
- practice regression and classification exercises in Jupyter notebooks
- study unsupervised learning and recommender system examples
- review quiz answers and markdown notes for the ML specialization course
- set up a local Python environment for the Coursera ML course

## When to choose
- you are taking or reviewing Andrew Ng's 2022 Machine Learning Specialization and want the labs and quizzes locally
- you prefer learning machine learning through runnable Jupyter notebooks
- you want community notes and slides organized by course part

## When to avoid
- you need a production machine-learning library or framework
- you want an official or fully verified source of course materials
- you need advanced or research-level ML content beyond an introductory specialization

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, tutorials, education, artificial-intelligence
- platform: python, cross-platform
- tags: andrew-ng, coursera, jupyter-notebooks, course-materials, supervised-learning, unsupervised-learning, reinforcement-learning, chinese

## Member repositories
- kaieye/2022-Machine-Learning-Specialization (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:54.960019+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 2026-08-29T18:19:43.888916+00:00, confidence not recorded.
  - readme: https://github.com/kaieye/2022-Machine-Learning-Specialization (fetched 2026-08-28T04:08:54.960019+00:00, sha 2a19ed609411)
- Data as of 2026-08-30T08:39:29.467469+00:00.