I like starting my machine learning classes with genetic algorithms (which we'll abbreviate "GA" sometimes). Genetic algorithms are probably the least practical of the ML algorithms I cover, but I love starting with them because they're fascinating and they do a good job of introducing the "cost function" or "error function", and the idea of local and global optima -- concepts both important and common to most other ML algorithms.
I love machine learning algorithms. I've taught classes and seminars and given talks on ML. The subject is fascinating to me, but like all skills fascination simply isn't enough. To get good at something, you need to practice!