# dibgerge/ml-coursera-python-assignments

Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

Repository: https://github.com/dibgerge/ml-coursera-python-assignments
Canonical: https://ross.abutalabs.com/products/ml-coursera-python-assignments
Language: Jupyter Notebook
License Family: other
Last push: 2023-06-18T14:32:22+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": 3119, "days_push": 1172, "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 5570, forks 2141 (observed 2026-08-28T04:09:22.462035+00:00)

## What it is
Python rewrites of the programming assignments for Andrew Ng's Coursera Machine Learning MOOC, delivered as Jupyter Notebooks with re-written instructions. The assignments support submission to the original Coursera grader and can be run locally or in an online Deepnote workspace.

## Use cases
- complete Andrew Ng's machine learning course assignments in Python instead of MATLAB
- practice implementing machine learning algorithms from scratch in Jupyter notebooks
- submit Coursera ML assignments for grading using Python code
- learn the Python machine learning ecosystem as a beginner
- work through course exercises in a browser without installing MATLAB or Octave

## When to choose
- you are enrolled in the Coursera Machine Learning class and prefer Python over MATLAB/Octave
- you want guided, notebook-based exercises with grading support
- you want to run assignments in a browser via Deepnote

## When to avoid
- you need a production machine learning library or framework
- you are not taking or following the Coursera course and want standalone tutorials
- you need actively maintained content with a license for redistribution

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, developer-tools
- domain: machine-learning, education, tutorials, data-science
- platform: python, cross-platform
- tags: coursera, jupyter-notebooks, andrew-ng, assignments, mooc, python-rewrite, education, web

## Member repositories
- dibgerge/ml-coursera-python-assignments (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:22.462035+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 2026-08-29T17:55:36.057780+00:00, confidence not recorded.
  - readme: https://github.com/dibgerge/ml-coursera-python-assignments (fetched 2026-08-28T04:09:22.462035+00:00, sha e36614bf901d)
- Data as of 2026-08-30T08:39:29.467469+00:00.