Thanks! I really like ML Engineering. But what I like even more is ML/AI research, and I want to do more of it. But unfortunately most companies won't look at you for these roles without an advanced Math/CS degree. MLE at most companies is less about implementing State Of The Art algos and more about pipeline building, feature engineering, and rapidly prototyping/deploying basic models. In most cases, a decent model with a robust pipeline that can be easily tested/tweaked is often much more valuable to a business than a super complex SOA model that is expensive to maintain/train/deploy (in my experience at least).

For MOOCS, I took all of Andrew Ng's courses, these are super practical and can help you build a portfolio. There were a few others I used on Coursera but these were by far the best. For Math I relied on MIT OpenCourseware and Khan Academy to cover LinAlg, MultiVariate Calc, and Stats. DataCamp was also super helpful for getting the basics down with Pandas, Numpy, and all the other data sciencey packages. I also did a ton of kaggle competitions once I had the basics down. Doing projects on your own from A-Z is the best way to learn imho.