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Requests for advice and / or encouragement. On basically any topic and for any scale of problem.
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Interesting. It came to about 5k for me, possibly because it most of it was review for me, and I blew through it in under a month, consequently not getting many scheduled reviews.
I also finished Methods of Proof, which was interesting although it focused a lot more on integer factorization than I expected, and am now about 80% of the way though math for machine learning. The more complicated linear algebra material was a bit of a speed bump for me, as I had to go back and do some reviewing to get a better understanding of what was going on. I should be ready for their ML course when it comes out, and maybe even have time to finish up the parts of the LA => MVC => Stats sequence not covered in M4ML.
Methods of Proof did not strike me (a programmer with many years of experience) as particularly relevant to software engineering. On the other hand, it's a very short course, I think about 1800 XP. Discrete math I think should be more relevant. You will definitely get questions about complexity analysis (Big-O) in interviews.
M4ML is a selection of the most ML-relevant lessons from the LA => MVC => Stats sequence. I think it covers about half of LA and stats, and a third of MVC. I chose to take M4ML first in order to benefit from the interleaving of topics, instead of doing the full sequence one subject at a time.
You can definitely do software engineering without all this math. I studied most of this in college and haven't really ever used it, except some concepts from discrete math. There are specific domains where it can be useful or even essential, but you can have a solid career and make a lot of money basically never using math. One of the best engineers I've ever met told me he almost failed out of college because he wasn't good at math.
I don't know much about how much math you need for ML, as I'm not an ML developer. I would start with discrete math and M4ML, just in case.
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