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https://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March...
Also in published form from Cambridge University Press:
https://www.cambridge.org/core/books/foundations-of-data-sci...
Are there obvious paths into these spaces for someone stuck over in devops/infrastructure/platform engineering? Or is it too far a hop to really find a direct path in? Let me ask a slightly different way - can someone like me get into a job like these, without needing some more college? My day job is wrapping up OS templates for people with ML software and I always wonder what they get to go do with them once they tu…
It is a trendy area and in such areas there is always skepticism towards wannabe entrants. As for whether you know enough math, I would start by watching the fast.ai videos and seeing if you're comfortable with the explanations and tools.
I can say I have a stronger math background than most programmers (though less strong than that of real math geeks) and I don't think I know enough math to really grok this stuff, but I'm always after a more foundational understanding than it takes to just use the tools. I think there are opportunities that don't require the math, but are just about having gotten some practice with packages X, Y, or Z. In the end though, those are like web frameworks that become obsolete all the time. So it is worth spending time on foundations.
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Do you have a link to the YouTube lectures? I'm taking a course and this is one of the books that we're using
It's linked off Trevor Hasties webpage see here: https://www.youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQy...
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The Elements of Statistical Learning, by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie. I’ve seen it referenced quite a few times and the TOC looks good.
This was one of the first books my advisor told me to read when I started my ML phd a...long time ago. The fundamentals of machine learning haven't changed and it's a great book.