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Learn the fundamentals of statistics first; then worry about the fundamentals of machine learning.

I'd greatly appreciate any good recommendations for text/ videos on statistics.

A good understanding of Stats IMO depends heavily on a solid basis in Probability (e.g. Pitman, Feller, Ross) which itself relies on a solid basis in Mathematical Analysis (e.g. Rudin). The hard work on the fundamentals now will really prepare you to do some serious damage later in ML. And if you really want to be prepared I would also read some elementary theory in Measure theory as well.

Seriously, this is different from coursework. You need some really strong motivation to cover all this through self study and stay on track. I believe the user should start simpler and build from there, both above and below.

Unfortunately, I don't have a well defined plan to recommend to the OP but any input from someone who has managed to learn the theory on their own can shed some light on how it is done. Especially on how to stay on track with a full time job on hand.

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