industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
A curated directory of machine learning and data science code notebooks organized by industry sector, linking to practical Python examples for healthcare, banking, agriculture, retail, and a dozen more fields.
This repository is a curated collection of machine learning and data science code notebooks organized by industry. Instead of focusing on theory, it gathers practical examples of how data science is actually being used in fields like healthcare, banking, agriculture, real estate, education, and roughly a dozen others. Each industry section links to Jupyter Notebooks and libraries written mostly in Python.
The collection was originally built in the style of "awesome" lists, which are popular on GitHub as community-maintained directories of resources on a given topic. Here the topic is industry applications of machine learning, and contributors have added notebooks covering things like fraud detection in banking, drug discovery in biotech, demand forecasting in retail, and similar real-world problems. The README contains a large table of contents broken down by sector and sub-sector.
The repository is connected to a company called Sov.ai, which works on applying machine learning to financial data and quantitative investing. As of 2024, the repository's maintainers were using it partly as a recruiting channel, seeking PhD-level researchers interested in financial machine learning projects. The README describes Sov.ai's work with quantitative hedge funds and lists sample research directions such as using satellite imagery or GitHub activity data to inform investment decisions.
For a non-technical reader, the most useful aspect of this repository is its breadth. If you want to know whether machine learning has been applied to a specific industry, and whether working code examples exist, this list is a practical starting point. The repository accepts contributions via pull requests or through a linked Google Sheet. The full README is longer than what was shown.
Where it fits
- Find working Python notebook examples for applying machine learning in a specific industry like healthcare or retail.
- Discover fraud detection code for banking or drug discovery notebooks for biotech as starting points for a project.
- Use the collection as a reference to understand which ML techniques are being applied in your field.
- Contribute a new industry notebook or link via pull request to help others find practical ML examples.