OpenMLU is an initiative to make an open and free education in Machine Learning available to anyone. We aim to do this by collecting the best online resources and curating them in our curriculum, so that people from various backgrounds have a reference point for where to start, what the next steps could be and how to continue your learning journey. As evident by our name, our curriculum is supposed to be reflective of the level of rigorousness in university classes, which is why every topic is studied with at least one lecture/exercise/project-based course and supplementary resources like books and blog posts. We try to provide as much guidance as possible while maintaining a flexible structure.
The way you use OpenMLU is completely up to you. The curriculum and suggested paths are simply recommendations from our personal experience. Use the resources in the curriculum however you like. Whether you want to strictly follow our curriculum or just sample courses to get a tase of ML, feel free to experiment with how you approach your own education.
Yes, it is 100% free. Every resource mentioned in our curriculum, whether course or book, is free.
Every resource recommended in the curriculum has been previously used by a member of the team. The focus of our curation process lies on evaluating as many resources as possible and selecting the most high-quality content for the curriculum. As different resources are suitable for different people coming from various background, we always try to select a diverse set of resources that is helpful for as many people as possible. We currently base our decisions on personal experience, so it definitely means we are not able to evaluate every single resource out there. Thus, if you see something missing or a mistake, let us know!
No. The curriculum will be continuously updated over time as new resources become available and old ones might become outdated.
A lot! There’s still so much content we’d like to add to our curriculum. Whether it’s resources for getting hired, ML systems design or interpretability, check out our “Request for Content”, for an overview of topics we’d love to see more published content of.
How to contribute
If you want to contribute content or make any suggestions, please create a GitHub pull request or issue on our project page.
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