# firmai/industry-machine-learning

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

Repository: https://github.com/firmai/industry-machine-learning
Canonical: https://ross.abutalabs.com/products/industry-machine-learning
Homepage: https://www.sov.ai/
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, jupyter-notebook, datascience, example, python, practical-machine-learning, data-science, firmai
Last push: 2024-10-04T12:55:18+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": 2679, "days_push": 698, "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 7486, forks 1249 (observed 2026-08-28T04:10:00.243847+00:00)

## What it is
A curated list of applied machine learning and data science notebooks and libraries organized across different industries. It serves as a reference collection of practical, real-world ML examples rather than a software tool itself.

## Use cases
- find practical machine learning notebook examples by industry
- learn applied data science through real-world case studies
- discover ML libraries used in finance, healthcare, and other sectors
- get inspiration for industry-specific ML projects
- find reference implementations of data science techniques
- explore alternative data and quantitative finance examples

## When to choose
- you want curated, industry-specific ML notebook examples to learn from
- you need real-world case studies rather than toy datasets
- you are exploring how ML is applied across sectors like finance and energy
- you want a starting point for applied data science projects

## When to avoid
- you need production-ready, maintained software with a license
- you want a framework or library to install and use directly
- you need guaranteed upkeep, as the list is link-based and links may rot
- you require formal documentation or support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, developer-tools
- domain: machine-learning, data-science, tutorials, awesome-lists, fintech
- platform: python, cross-platform
- tags: awesome-list, jupyter-notebooks, applied-machine-learning, industry-applications, curated-list, example-notebooks

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:00.243847+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 2026-08-29T17:37:35.278352+00:00, confidence not recorded.
  - readme: https://github.com/firmai/industry-machine-learning (fetched 2026-08-28T04:10:00.243847+00:00, sha 5b70dec219fc)
  - homepage: https://www.sov.ai/ (fetched 2026-08-29T08:32:49.812847+00:00, sha c80f25dcd4fa)
  - site_page: https://sov.ai/faq (fetched 2026-08-29T08:32:49.815445+00:00, sha 0e5d5e71a9ac)
- Data as of 2026-08-30T08:39:29.467469+00:00.