A 2020 Vision of Linear Algebra
I like all Strang's books... at least the ones I have.
I don't have his Learning from Data book, yet... however.<p>I also really like the applied linear algebra book by Boyd Vandenberghe:
<a href="https://web.stanford.edu/~boyd/vmls/" rel="nofollow">https://web.stanford.edu/~boyd/vmls/</a>
Free PDF is available on their website.
There is Julia and Python code companions for the book and lecture slides from both Profs their websites.  Also check out their other books, many of which have free PDF's available.<p>I can also recommend Data-Driven science and engineering by Brunton and Kutz.
<a href="http://databookuw.com/" rel="nofollow">http://databookuw.com/</a>
There used to be a free preprint PDF of the book but I can't find it now.  Book is totally worth picking up... MATLAB and Python code available. Steve Brunton's lectures on YouTube are pretty damn good and compliment the book well:
<a href="https://www.youtube.com/channel/UCm5mt-A4w61lknZ9lCsZtBw/featured" rel="nofollow">https://www.youtube.com/channel/UCm5mt-A4w61lknZ9lCsZtBw/fea...</a><p>Another really cool book is Algorithms for Optimization by Mykel Kochenderfer and Tim Wheeler: <a href="https://mitpress.mit.edu/books/algorithms-optimization" rel="nofollow">https://mitpress.mit.edu/books/algorithms-optimization</a>.  Julia code used in book.