Linear Algebra for Machine Learning by Jason Brownlee.
From the post:
You do not need to learn linear algebra before you get started in machine learning, but at some time you may wish to dive deeper.
In fact, if there was one area of mathematics I would suggest improving before the others, it would be linear algebra. It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms.
In this post we take a closer look at linear algebra and why you should make the time to improve your skills and knowledge in linear algebra if you want to get more out of machine learning.
If you already know your way around Eigen Vectors and SVD decompositions, this post is probably not for you.
Another great collection of resources from Jason!
As usual, a great collection of resources is only the starting point for learning. The next step requires effort from the user. Sorry, wish I had better news. 😉
On the upside though, rather than thinking of it as boring mathematics, imagine how you can manipulate machine learning if you know linear algebra.
Embedding linear algebra in a machine learning book that is written from a battle perspective between different camps could be quite engaging. For that matter if online, exercises could be part of an e-warfare environment.
Something to think about.