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I want to delve into Machine Learning, which Linear Algebra book will be a good foundation for that purpose? I have heard about Axler's book, how's that ?

The Limit Does Not Exist
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    Axler's book is great as an introduction to the theoretical framework of linear algebra and as stepping stone to functional analysis, but it (intentionally) underemphasizes matrix computation. For the purposes of Machine Learning, I would recommend Gilbert Strang's Introduction to Linear Algebra instead. – Ben Grossmann May 12 '21 at 15:43
  • Check https://math.stackexchange.com/questions/250370/linear-algebra-book-satisfying-those-reqs?rq=1 or I guess handful of related texts. Personal opinion: Axler's book is an excellent resource for learning Linear algebra. If you wanna know enough for ML, then maybe have a look at the relevant chapters of Deisenroth's Math for ML (And maybe Goodfello et al 's DL book, first chapters) book and make sure you understand \textbf{everyting} by using any online/book resource since that's the bare minimum....... – Jack May 12 '21 at 15:44
  • .... As someone who struggled with understanding the linear algebra of ML, I'd also recommend investing some time (few months) reading Godemont's Algebra (old book) and read it cover to cover. The actual linear algebra is at the end, but the begining chapters will give you enough maturity in basic abstract algebra that could allow you to get the ideas of linear algebra more deeply (and much quicker) than you'd otherwise learn from just a linear algebra book. Then it'd be much better to pick any linear algebra (e.g. Axler) and understand everything from it. – Jack May 12 '21 at 15:46
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    For machine learning, I'd say that Axler is too theoretical (although you should read it at some point to understand the material deeply). I second the recommendation for Strang's Introduction to Linear Algebra. You might also try Strang's recent book Linear Algebra and Learning from Data. – littleO May 12 '21 at 15:52

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In my opinion:

Linear Algebra Done Right by Sheldon Axler is high level... you can say it is second

course in linear algebra not introductory.

For Introductory course in Linear Algebra the following are great:

1)Elementary Linear Algebra Applications Version by Howard Anton, Chris Rorres, Anton Kaul

2)Elementary Linear Algebra Stephen_Francis_Andrilli Fourth Edition

these two books are very great in my opinion.

Mahmoud albahar
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    I am starting to read David Lay's book : "Linear Algebra and It's Applications", although I can just go with Howard Anton "ELementary Linear Algebra". Serge Lang and Sheldon Axlers' are typical for Math majors wanting for rigors with theorems-proofs with all abstract things introduced from the start; that's why they begin with Vector Spaces in the 1st chapter. There is one book (an old book) that is quite rigorous : "Linear Algebra" by Kenneth Hoffman and Ray Kunze. Hoffman is from MIT, so we can expect a good text. It begins with Linear Equations, followed by Vector Spaces. – Lex Soft Jul 08 '22 at 06:28
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I'm not sure that this book is translated (or maybe it was in French from some other language) , but I had my teacher base his linear Algebra course on "Algèbre Linéaire, 4eme édition, by Joseph Grifone" and I found it very very good. I think it covers basic to advanced Linear Algebra.

GenO
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