Allen Downey (author of the above) has a number of books on computer science-y things. You can buy hardcopies but I think all of them are also just freely available.
I think at the beginning of learning LA I would have benefited from a more broad introduction to the topic by explaining that it is the algebra of transformations, generally linear transformations, and also the art of quantifying those transformations in meaningful ways.
I would have benefited from some more handwaving in this regard (matrix multiplication, eigenvectors and eigenvalues) and less on the mechanics of the operations, before starting on the basic technicalities. But a “lesson” on these topics on day 0 is too soon
Here's a few:
Think Complexity
https://github.com/AllenDowney/ThinkComplexity2
Think DSP
https://github.com/AllenDowney/ThinkDSP
Think Stats
https://github.com/AllenDowney/ThinkStats/
Think Bayes
https://github.com/AllenDowney/ThinkBayes2/
Many places on the web. Runestone is probably the most useful like but I’ll leave my favorite classic one below.
http://www.openbookproject.net/thinkcs/python/english3e/
That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
I would have benefited from some more handwaving in this regard (matrix multiplication, eigenvectors and eigenvalues) and less on the mechanics of the operations, before starting on the basic technicalities. But a “lesson” on these topics on day 0 is too soon