# jdwittenauer/ipython-notebooks

A collection of IPython notebooks covering various topics.

Repository: https://github.com/jdwittenauer/ipython-notebooks
Canonical: https://ross.abutalabs.com/products/ipython-notebooks
Language: Jupyter Notebook
License Family: other
Last push: 2020-10-19T12:44:35+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": 4477, "days_push": 2144, "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 2593, forks 1476 (observed 2026-08-28T04:07:03.006126+00:00)

## What it is
A collection of IPython/Jupyter notebooks covering Python, popular data science libraries (NumPy, Pandas, Scikit-learn, etc.), and machine learning exercises from Andrew Ng's Coursera course. It serves as a self-study tutorial resource rather than a software tool.

## Use cases
- learn python data science libraries through notebooks
- work through andrew ng machine learning exercises in python
- find example notebooks for pandas and numpy
- study machine learning algorithms with worked examples
- learn ipython magic commands

## When to choose
- you want hands-on notebook tutorials for the Python data science stack
- you're following Andrew Ng's ML course and want Python implementations of the exercises

## When to avoid
- you need production-ready or maintained software
- you need a library with a license for redistribution
- you want up-to-date examples for current library versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning, data-visualization, nlp
- domain: data-science, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, tutorials, numpy, pandas, scikit-learn, coursera-exercises

## Member repositories
- jdwittenauer/ipython-notebooks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:03.006126+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 2026-08-30T02:22:16.559292+00:00, confidence not recorded.
  - readme: https://github.com/jdwittenauer/ipython-notebooks (fetched 2026-08-28T04:07:03.006126+00:00, sha f1dba1200c72)
- Data as of 2026-08-30T08:39:29.467469+00:00.