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I am not machine learning engineer, so can't say much about the resources or roadmap but I want to say that when studying ML, don't get much lost in studying a lot of concepts. Rather learn some basics and then start applying it. Think of some project and learn just the required things and work on the project.
I am suggesting this way because I experienced similar situation iny BTech. That time ML had just started booming, people were not much aware of it. Neither there were many resources. I asked one friend who went to do Phd in machine learning domain. He told me all the topics to study like Algebra, calculus, statistics, EDA etc etc etc. I studied all required mathematics topics for about 2 months from Stanford and MIT lectures as suggested by that friend. Then saw University lectures of Andrew NG and so on. This way I studied around 4 months and didn't actually implemented any ML algorithm. Thus I would suggest you don't do this way.
Target for companies like Deepmind, OpenAi etc which are more focused on ML reasearch rather than just using some algorithm to predict something.