I am not quite sure that by following the advice that we don't require to understand the math behind the techniques of ML will take us far in this field. Going to university is not the only solution to dig deep but restricting our understanding only to the point where we can explain it to the business is a very shallow approach. I believe the person who is making models should know in and out of it else human, who has made ML, will himself becomes ML dependent. Even university courses are not following this approach. The agenda of today's market is to just introduce you to these fancy terms and left you in the sea to drown. For instance: There was a stats course in a university where a professor was teaching that p value should be less than 0.05 without even informing students what p value is. The need of an hour is not to rush but to give yourself time to understand small things such as vectors, matrices, probability, statistics. In the end, you could be slow but confident about your decisions.
1 post • joined 25 May 2018