It&#x27;s enough tasks where you need to have understanding of the ML algorithms&#x2F;workflows&#x2F;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 &amp; 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 &quot;Hidden Technical Debt in Machine Learning Systems&quot; paper (<a href="https:&#x2F;&#x2F;papers.nips.cc&#x2F;paper&#x2F;5656-hidden-technical-debt-in-machine-learning-systems" rel="nofollow">https:&#x2F;&#x2F;papers.nips.cc&#x2F;paper&#x2F;5656-hidden-technical-debt-in-m...</a>) quite useful for understanding).<p>I personally took several ML courses from coursera&#x2F;udacity&#x2F;edX, and they helped me when I decided to move to another group that works on the ML-related projects.