It's enough tasks where you need to have understanding of the ML algorithms/workflows/tools, and be able to implement production system that integrates them into real systems, generating value for companies. In many cases you need to have very good domain knowledge & software development skills in addition to understanding of ML. And in ML-related systems, the big part of implementation not ML itself, but a lot of supporting stuff (figure 1 from "Hidden Technical Debt in Machine Learning Systems" paper (https://papers.nips.cc/paper/5656-hidden-technical-debt-in-m...) quite useful for understanding).

I personally took several ML courses from coursera/udacity/edX, and they helped me when I decided to move to another group that works on the ML-related projects.