Well I can't blame you - I have thought for years myself that I could 'pick up machine learning' like it was some web programming framework that you can be an e

Well I can't blame you - I have thought for years myself that I could 'pick up machine learning' like it was some web programming framework that you can be an expert at over a weekend. But I had to concede after a few years of trying on and off that it doesn't work like that. Which is also why it's a testy subject for me - like how ex-smokers are usually the most obnoxious anti-smoking crusaders. I don't have a proper math-intensive CS degree myself, only a 'software engineering' degree, so I've been going back to uni and studying when I can to build a math foundation first. So what I wrote down above is basically the 'curriculum' I designed for myself, as well as what I already know from the software side and is needed for ML. To make it relevant again to the topic at hand - this is also why a university degree (and a proper one) is required for programming jobs that are cutting edge. Sure, making websites doesn't require it - and neither do line of business CRUD applications. The interesting work though is not 'programming', it's 'using math to solve problems and using programming to get the computer to do the repetitive stuff for you'. When it's framed like that, it becomes obvious why a university degree is required. (yes yes theoretically it's possible to study math by yourself - the number of people who have the discipline to do that though is minuscule compared to the number of people who can bootstrap themselves to be 'programmers' of simple applications like web applications, most mobile apps or CRUD applications)