# patchy631/machine-learning

Repository: https://github.com/patchy631/machine-learning
Canonical: https://ross.abutalabs.com/products/patchy631-machine-learning
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-11-10T15:08:00+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": 1554, "days_push": 661, "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 1548, forks 294 (observed 2026-08-28T04:05:01.905778+00:00)

## What it is
A collection of Jupyter Notebook tutorials covering machine learning topics from Python basics to LLMs and MLOps, accompanying the author's Twitter tutorials. It is educational material rather than a reusable software library.

## Use cases
- learn machine learning with python notebooks
- tutorial on large language models
- get started with pytorch and tensorflow
- learn pandas and numpy for data analysis
- understand mlops best practices
- computer vision examples for beginners
- nlp tutorial code

## When to choose
- you want hands-on notebook examples for ML concepts
- you follow the author's Twitter tutorials and want the code
- you are a beginner exploring ML, NLP, CV, or LLMs

## When to avoid
- you need a production-ready ML library or framework
- you need maintained, tested software to depend on
- you want a structured course with assessments rather than example notebooks

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, nlp, computer-vision, data-visualization, deep-learning
- domain: machine-learning, deep-learning, tutorials, data-science, computer-vision
- platform: python
- tags: jupyter-notebooks, tutorials, mlops, llms, pytorch, tensorflow, pandas, numpy, matplotlib, natural-language-processing

## Member repositories
- patchy631/machine-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:01.905778+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 2026-08-30T04:30:26.255785+00:00, confidence not recorded.
  - readme: https://github.com/patchy631/machine-learning (fetched 2026-08-28T04:05:01.905778+00:00, sha f0bb154a3800)
- Data as of 2026-08-30T08:39:29.467469+00:00.