This is well-written and I applaud any step toward demystifying the sometimes scary sounding concepts that drive much of the ML algorithms.
Knowing you can pretty quickly whip up a KNN or ANN in a few hundred lines of code or fewer is one of the more eye-opening parts of the delving in. For the most part, supervised learning follows a pretty reliable path and each algorithm obviously varies in approach, but I know I originally thought "deep learning? ugh, sounds abstract and complicated" before realizing it was all just a deep ANN.
Long story short: dig in. It's unlikely to be as complex as you think. And if you've ever had an algorithms class (or worked as a professional software dev) none of it should be too daunting. Your only problem will be keeping up the charade if people around you think ML/AI is some sort of magic.
Knowing you can pretty quickly whip up a KNN or ANN in a few hundred lines of code or fewer is one of the more eye-opening parts of the delving in. For the most part, supervised learning follows a pretty reliable path and each algorithm obviously varies in approach, but I know I originally thought "deep learning? ugh, sounds abstract and complicated" before realizing it was all just a deep ANN.
Long story short: dig in. It's unlikely to be as complex as you think. And if you've ever had an algorithms class (or worked as a professional software dev) none of it should be too daunting. Your only problem will be keeping up the charade if people around you think ML/AI is some sort of magic.