Coming back to this, I totally get why youre stressed about the Greek symbols. I was in the same boat last year and honestly... it looks way scarier than it is. @Reply #1 - good point about the linear algebra, but ngl, I think statistics is actually what youll use most when youre actually looking at data. I've been sticking with No Starch Press Practical Statistics for Data Scientists 2nd Edition for a while now and I am super satisfied with how it explains things without getting too bogged down in pure theory. No complaints so far, and it really helps with the job-ready side of things. For the calculus part, dont worry about solving massive equations on paper like in school. You mostly just need to understand the concept of a gradient which is basically just how a function changes. Its what helps the models learn by finding the lowest error. I have found that Wellesley-Cambridge Press Introduction to Linear Algebra 6th Edition by Gilbert Strang is also a really safe bet if you want to be sure youre getting the fundamentals right. Its a classic for a reason and very reliable for beginners who need a solid foundation. Since youre aiming for December, just focus on the why behind the math rather than memorizing every formula. Youre gonna do fine, just take it one chapter at a time and dont let the notation freak you out. If you need more help with specific topics just shout!

Linear algebra is essential. I'm satisfied using:

• Pearson Mathematics for Machine Learning 1st Edition: Great theory, lacks code.
• O'Reilly Hands-On Machine Learning 3rd Edition: Excellent code, less math. Both work well.

Gonna try this over the weekend. Will report back if it works!