[tmarthal]

Most likely because if you are doing ML and do not have a PhD (or previous experience), you are just looking at calling a library function that you do not understand. The majority of 'machine learning meetups' (not in the Bay Area), are attended by programmers that are looking to figure out how to call an R package to give them recommendations or similar items in a list (clustering).

edit I just read the other replies to this post. I believe that most startups with Machine Learning teams are doing more than just calling R-libraries; most development work that I've done for myself and teams has been for tooling and operationalizing data infrastructure (i.e. data engineering, not data science). However if you need a simple recommendation for an app then calling the library methods without 100% understanding may be enough (but calling library methods without underlying understanding is a bad trait in a programmer (e.g. calling the sort() function without understanding quicksort)).

[B1gred]

I am a statistician working in the data sciences. I see lots of programmers show interest but do not have the depth of knowledge in mathematics and statistics. They can apply libraries and do 80% fine but do not have the educational background to side step assumptions and pitfalls.

[TheOtherHobbes]

>Most likely because if you are doing ML and do not have a PhD (or previous experience), you are just looking at calling a library function that you do not understand.

I'm assuming that's your interpretation of the industry mindset, and not a view you personally hold.

IMO it's a naive assumption, and it would be trivial to test for it in an interview.

I have an interest in ML for a specific domain, no PhD, and it wouldn't cross my mind to try to use it as a set of shrink-wrapped library functions.