There's Andrew Ng's Coursera class and the related classes if you haven't seen that yet. I think there's a full specialization now. He's also got a decent starter PDF called Machine Learning Yearning.

I've heard that the Fast.ai lectures are good, though I haven't watched them myself.

Google has some great online reading. I like the People + AI guidebook cause it focuses on how to apply machine learning, and that's an area that's often overlooked.

Kaggle and other online competitions are a great place to learn and grow. I'd suggest starting with some of the easy ones that have tutorials, and then looking for competitions that you're passionate about. For instance, years ago I ran into a competition run by the European Space Agency -- that motivated me to push harder and learn more.

If you can find projects to team up with others, that will help you a lot as well. DataKind is an example of that, but I don't think they have much ML work. I'm not sure if hackathons still exist but those can be another great way to learn quickly.

To get inspiration about projects that may be relevant for your current role, I'd suggest doing some searches on Google Scholar and reading those papers, then finding the papers they cite that are interesting. And then finding the most popular papers that cite them. There's almost certainly some interesting work in your area and the trick is figuring out what things are called so you can search.