# nsoojin/coursera-ml-py

Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

Repository: https://github.com/nsoojin/coursera-ml-py
Canonical: https://ross.abutalabs.com/products/coursera-ml-py
Language: Python
License: MIT
License Family: permissive
Topics: andrew-ng-course, andrew-ng-ml-course, coursera-machine-learning, andrew-ng-machine-learning, numpy-exercises, machine-learning-ex1, neural-network, support-vector-machines, principal-component-analysis, logistic-regression, anomaly-detection, python-ml
Last push: 2020-09-02T17:38:18+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": 3452, "days_push": 2191, "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 1435, forks 489 (observed 2026-08-28T04:04:43.467193+00:00)

## What it is
Python re-implementations of the programming assignments from Andrew Ng's Coursera Machine Learning course, originally written in Octave/Matlab. It covers eight exercises spanning linear and logistic regression, neural networks, SVMs, k-means, PCA, anomaly detection, and recommender systems using numpy, scipy, and matplotlib.

## Use cases
- redo Andrew Ng's Coursera ML assignments in Python instead of Octave
- practice implementing machine learning algorithms from scratch with numpy
- learn how linear regression and logistic regression work step by step
- study worked examples of neural network forward and backpropagation
- find reference implementations of SVM, k-means, and PCA exercises
- prepare for machine learning coursework with guided exercises

## When to choose
- you completed or are taking the Coursera Machine Learning course and want Python versions of the exercises
- you want educational, step-by-step numpy implementations of classic ML algorithms
- you prefer plain ndarray code without numpy.matrix

## When to avoid
- you need a production-ready machine learning library
- you want modern course content like the updated deep learning specialization
- you need actively maintained code compatible with the latest Python versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python, cross-platform
- tags: coursera, andrew-ng, numpy-exercises, course-assignments, octave-to-python, neural-networks, support-vector-machines, logistic-regression, k-means, principal-component-analysis, anomaly-detection

## Member repositories
- nsoojin/coursera-ml-py (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:43.467193+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 2026-08-30T04:36:52.145945+00:00, confidence not recorded.
  - readme: https://github.com/nsoojin/coursera-ml-py (fetched 2026-08-28T04:04:43.467193+00:00, sha af04a89fc60f)
- Data as of 2026-08-30T08:39:29.467469+00:00.