I've also been self-teaching myself similar topics. Reading books and working through the exercises works much better for me personally than watching videos. For Python, I recommend Think Python 2e, which is freely available here, and Charles Severance's Python for Everybody on FreeCodeCamp. For Machine Learning, the gold standard is An Introduction to Statistical Learning. The exercises are in R, but I think you can find Python versions somewhere on the internet.

For Statistics and Probability I used OpenIntro, but I've also got Pishro-Nik's Introduction to Probability, Statistics, and Random Processes on my list as a more advanced next book. For Differential Equations and Linear Algebra, I'm using Strang's book of the same name, and the associated MIT OCW lectures.

Send me a PM if you'd like to discuss further!

What textbooks would you recommend for these topics? (Right now my list is only “Linear Algebra Done Right”)

I would recommend not starting with Linear Algebra Done Right unless you already know the basics of linear algebra. The book does not cover some basic material (like row reduction, elementary matrices, solving linear equations) and instead focuses on trying to build up the theory of linear algebra in a "clean" way, which makes it enlightening as a second or third exposure to linear algebra but a cruel way to be introduced to the subject for the first time. I think 3Blue1Brown videos → Vipul Naik's lecture notes → 3Blue1Brown videos (again) → Gilbert Strang-like books/Treil's Linear Algebra Done Wrong → 3Blue1Brown videos (yet again) → Linear Algebra Done Right would provide a much smoother experience. (See also this comment that I wrote a while ago.)

My advice for math is that it's often possible to think you understand something even if you don't, so it's good to do at least some exercises. Also, the methodology, and general "mathematical maturity" is often what you'll reuse the most in research - being able to reason by following specific allowed/disallowed steps, and knowing that you can understand a claim by reading Wolfram Mathworld, Wikipedia, textbooks, etc. So to some extent it doesn't matter so much what you learn, as that you learn something well. Having said that, the first half of a math textbook tends to be much more useful than the second half - there are diminishing returns in each subfield.

For programming, the same is often true - what you're aiming to get is a general sort of maturity, and comfort with debugging and building programs. So probably you want to mostly read tutorials for initial few weeks, then mostly do a project after that.

In both cases, I agree that a tutor is super-useful for getting unstuck, if you have the luxury of being able to afford one.

A couple of things which will be really helpful:

Not at the start but very useful for once you're starting to read papers.

IMHO the single best educational youtuber - There's a series on linear algebra, another on neural networks, and even one on calculus. 

The videos on their own won't be enough to learn, but they are a fantastic supplement and visualisation prompt.

What textbooks would you recommend for these topics? (Right now my list is only “Linear Algebra Done Right”)

The best textbooks on every subject from lesswrong.

IMO, talking with someone experienced is very much worth it, even if only to understand better what to study in your situation. Or to get feedback on your exercise sheet write-ups.

Besides, I strongly recommend you find someone at your level who wants to learn these subjects as well, so you can meet regularly and discuss exercises/unclear points for free. Discussing your half-formed ideas is more fun and results in faster learning if you can unstick/learn by explaining to each other.

Some more points:

1. The core of learning math and theoretical CS is doing exercises/working with the concepts in your mind, writing down your proofs, and getting hints/corrections from time to time, rather than reading, watching, or rote-memorizing something. The idea is that, while doing that, one processes the material enough to become comfortable with it...
2. When learning a subject from the ground up, I prefer studying from lecture notes to studying a book for several reasons:
   1. They show how many "credit points" they are worth, so you roughly know how much time you'd spend on them. You can also get a better idea of the prerequisite knowledge.
   2. As mentioned by RyanCarey, studying all of a book hits diminishing returns. But making a good selection is hard to impossible - unless you are a professor preparing lecture notes.
   3. You get a better model of your knowledge after working through an entire lecture vs a part of a book. You can use this to understand what material you are ready for next, job interviews etc.

Active learning (as in brilliant.org) seems worth a try to me as well, but I don't know if it is comprehensive enough.

Usually, lecture notes are based on (and share notation with) one or a few books, which you can fall back on if you don't understand something.

I'd be happy to tutor you; I'll PM you more information about me :)