Machine learning: an overview

Machine learning is becoming a buzzword, everybody talks aboit it and few seem to be interested in the math underneath (I find statements like "I wanted to know more but all sources were too statistical/mathematical and I wanted more practical stuff"). Let me tell you something: You can't really use Machine Learning if you don't know the statistical/mathematical basis. I am really upset when I see a Youtube video of some guy in T-Shirt probably working at a large organization ranting about Machine Learning and Data Science, telling programmers that maths is easy to grasp. Everybody knows how to press a button or, if you force me, almost everybody knows how to fix something in their Windows control panel, but that does not mean we can trust them when talking about building a secure payment system, Everybody can use Mahout or the like but that does not mean he knows jack about what he is doing using Naive Bayes to predict the class from thre variables (x, y, z) where z x 2 and x belongs to the range [-1,1]. Machine Learning is just a fancy word for the statistical/mathematical tools lying underneath, whose objective is to extract something that we may loosely call knowledge (or something that we understand) from data (or something chaotic that we do not understand), so that computers may take action based on the inferred knowledge.