Re: No need to go to Uni....
As someone who has a PhD in ML and working with lots of startups I can tell you that what i see right now is somewhere between funny and scary. People seem to think that there is a ML black box that they add to their product and suddenly all problems are solved.
This is often far from the truth. Mathematics is used in ML and related fields for a reason: it is a - somewhat- formal language to express and discuss ideas, free from semantic misinterpretations.
Of course anyone with half a brain can understand the basic idea of a decision tree or the formula given in the article. But to know when to apply which algorithm is already a question that is not easy to answer. For example, if the events A and B are independent, then using conditional probabilities as in the formula given in the article is useless. Pretty much every single algorithm I know has certain conditions attached to it which make it work well or maybe not at all. Those conditions are again mathematical concepts, like independence of events. Not knowing about those conditions means that basically the quality of the algorithm choice comes down to luck or what is on our calendar, next to solar storm as your excuse of the day.
Anyway, all I really want to say that even though the basic ideas of a certain algorithm might be simple, that does not mean that being able to use those algorithms effectively is simple, in the same sense that using a knife is simple to use, but carving a beautiful statue is not. And hence courses that aim to produce good AI/ML developers will need to have a certain amount of complexity.