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What kind of math background do you need to understand Sheaf theory? Are these lectures truly accessible for someone with a non-background?

I tried watching bits of the youtube video OP linked but never saw where he actually defined what a Sheaf was as oppose to building motivation of learning it for applications.

I am excited for any truly accessibly beginner resources. Want to get your opinion if you links are for the true beginner?

Excellent, thanks for you recommendation!

Thanks for the link.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

vs

https://www.amazon.com/Introduction-Statistical-Learning-App...

The former seems to assume prior knowledge and the latter seems more beginner friendly, except it uses R (in case anyone wants the distinction).

Based on Amazon reviews recommending this book to a beginner is like recommending rudin to a high school junior that wants to learn calculus for the first time. Is there a better book?

What are good online sources for a beginner to learn calculus, linear algebra and stats/probability needed for machine learning?

I don't want courses that teach math by coding but actual math courses. A lot of these online ML courses treat math as a black box so you never fully understand what's going on & a lot of math based ML courses are too advanced.

So I'd rather focus on understanding the math basics before diving into ML. So what do you guys recommend?

Thanks for the recommendations. Esp. Professor Leonard. Never knew such videos existed.

Thanks for the link. The last two weeks of the course pique my interest esp. the "Introduction to Probability Theory" section. Is this apart of a sequence of courses in data science? It wasn't clear to me if this is a standalone course or apart of a larger sequence.