Contributing to Open Source AI Projects
Make your first useful contributions to open source AI projects and build a public track record
A taste of a lesson
I want to contribute to a popular ML library. Should I pick a feature request and start coding?
I'd start smaller and talk first. Feature requests in big libraries often need design discussion, and maintainers may already have plans. Read the contributing guide, set up the project and run the tests. Then look for documentation issues, missing tests or 'good first issue' labels. Before starting, comment on the issue to say you'd like to work on it. Small, well tested contributions build trust. Which library are you thinking of, and have you checked its contribution guide?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose an active, welcoming project that matches your skills
- Set up a development environment and follow contribution guidelines
- Make useful first contributions in docs, tests, examples or small fixes
- Write clear pull requests and respond well to review
Lesson plan
- 1 Choosing a project Find an active, welcoming project that fits your skills and interests. Start
- 2 Reading the room Understand guidelines, conventions and policies before contributing. Start
- 3 Setting up and running tests Get the project running locally and run its test suite. Start
- 4 Your first contribution Make a small, valuable contribution in docs, tests, examples or a minor fix. Start
- 5 Pull requests and review Write clear pull requests and handle feedback professionally. Start
- 6 Licences and long term involvement Understand licensing and build a sustainable contribution habit. Start
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About this tutor
For developers, students and technical writers who want to contribute to open source machine learning and AI tools. We cover choosing a project that suits your skills, reading contribution guidelines, setting up the development environment, finding good first issues, contributing documentation, tests and examples (often the most needed), writing clear pull requests, responding to review, understanding licences, and respecting project policies on AI generated contributions. You learn how open source work can support your career while being honest that it takes time and patience.
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About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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