They’re much narrower in scope than a full curriculum (or even a course, for that matter), but Better Explained [0] has some very good overviews of math topics. They are a useful supplement I’ve come across repeatedly while searching topics I found challenging over the years.

On the topic of open source learning, I take every chance I can to heartily recommend fast.ai’s course [1]. It’s a good intro to Deep Learning that leaves you informed enough to build things, and equips you to ask follow-on questions and dive deeper when/where you need to.

[0]: https://betterexplained.com/

[1]: https://course.fast.ai/

Perhaps more relevant for this topic is the computational linear algebra course from fast.ai:

https://www.fast.ai/2017/07/17/num-lin-alg/

It has a lot more detail on stuff like floating point storage, memory layout, sparse matrices, iterative methods, etc than most linear algebra courses, but doesn't go much in to proofs, geometric interpretations, and other stuff that's less needed for algorithm design and implementation.

(Disclaimer, I'm from fast.ai.)

For AI/ML, I think https://madewithml.com/ does the job. It has curated open source projects, courses, papers and topics.

Something like Kahn Academy (https://www.khanacademy.org/)?

Khan Academy is great, but I mean for all resources on a subject. In other words, a place where people can post curations of resources, not actual content. So say I wanted to learn linear algebra it would point to Khan Academy for the basics part and then other places as well.

Something like this https://github.com/rossant/awesome-math ?

The list of "awesome lists" on github [1] is generally quite helpful (haven't checked them all myself obviously).

[1] https://github.com/sindresorhus/awesome