Complete linear algebra
theory and implementation in code

If you like hands-on/computational, I really enjoyed https://www.udemy.com/course/linear-algebra-theory-and-implementation/

Code in matlab and python

You should definitely take it if you have even a half hearted interest in the field.

As you likely know, if you're interested in geostatistics then you'll need to have a decent handle on MATLAB and additionally python.

This Udemy class is a phenomenal guide to the concepts covered in inverse theory when implementation in MATLAB since inverse theory is largely just applied linear algebra. They give cover skilled and exercises and for $11 (I think, I already bought it so the current price isn't showing up for me) it's a causative resource even though my professor is brilliant, he's often occupied or traveling and doesn't have a lot of time to go over the MATLAB portion of the class. Maybe your professor won't use MATLAB, but they would be doing you a disservice if they didn't and you should still use this as a resource.

They also have a ton of other classes from into to Python to digital signal processing using MATLAB and Python.

https://www.udemy.com/course/linear-algebra-theory-and-implementation/

Like I said in my prev comment, try and find a resource in python where you primarily code up the algebra. It will save you a lot of time and effort, for ML you mostly need intuitions and not proofs.

These two resources on Udemy are golden, the prof is really good:

https://www.udemy.com/course/math-with-python/

https://www.udemy.com/course/linear-algebra-theory-and-implementation/

After you've finished these two you could go into a bit of statistics and then traditional machine learning, finally you should go for deep learning.

Try and do a 50/50 split of theory and practice as well. When I was doing my masters in AI I focused too much on passing my courses (= theory) and not enough on implementation. Don't do the inverse either and code-up everything without understanding anything :)