[roel_v]

'AI' is not programming, it's mathematics (well the current 'flavor' of statistics-based AI, that is - the 1980's style AI people I used to work with were philosophers, legal scholars and the like). Anyway, there is no 'bridging the gap' - you need to start from a good foundation of statistics (and the 'prerequisites' - algebra, calculus, linear algebra) and in the end, the technicalities of the software and the theory come together naturally.

(source: have tried to 'bridge the gap' for 2 year, including taking MSc courses, before admitting to myself that it's a lost battle. Am now starting to build a solid math foundation before revisiting ML applications.)

[afshin]

This is exactly my experience as well. I'm an alright programmer, but it is insufficient, because machine learning and AI are a table resting on four legs: linear algebra, calculus, statistics, and programming. I've also found myself going back to build up those foundations.