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.
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.)