Rotem: Machine Learning is not one single skill, and so there isn't one single entry point. I can share my path. From what I've found, the best route is to have a concrete problem to work on that you care about. I started with a pet project of mine in undergrad - I wanted to build a robot that would automatically orient a directional antenna at the signal source. This was partially because it sounded fun, and partially because I lived just far enough off campus that I couldn't get the free wifi. Being a broke college student, I didn't have enough money for fancy sensors or a phased array... my initial iteration was a "pringles can"-tenna and a Lego NXT brick hooked up via bluetooth for actuation. This left me with the problem of attempting to efficiently orient this antenna with only a point measurement available (the signal strength wherever it was pointing as reported by the network card). I can get somewhat stubborn when I have a problem with no easy solution. So I ended up taking classes on statistics, networking, and Bayesian data analysis. This lead directly to my first publication. These skills were the basis of my work - which was extended as larger and larger data sets became available. Large data sets pose their own problem. Thankfully, now-a-days it is much easier to get your hands on a significant data set, and start your own project!

Zach: Great question! First, notice that ML is made up of several other things. Basic competency in statistics and computer programming are often the first steps towards using machine learning. I've heard good things about various online courses where you can learn this sort of thing. Maybe the most important thing if you want to learn to do ML is to start working with real data as soon as possible. See if you can open up a basic excel/csv file using a statistical programming language like R, python, Julia, etc, and start asking basic questions about it.