During my first year of studying machine learning, I've read a lot of papers, book chapters, and online tutorials. I like to learn by example. For me, theory paired with an implementation is the best way to learn a topic in machine learning. However, nearly every tutorial I've come across has a lot of one and little of the other. The ones that include both are usually presented at such a high level that they are of little use to someone trying to fully understand the topic at hand, or their code isn't even public!
Over the past few years, I've collated a lot of material from different sources to create a set of "primers" that are introductions to a wide array of machine learning topics. They include a theoretical foundation paired with a short tutorial with accompanying code written in Python. After going through one of these primers, you should have enough basic knowledge to begin reading papers about a particular subject without feeling too lost.
I'm currently in the process of posting these primers. Most of my original code was written in MATLAB, but it's not free and not everyone has access to it through their university or workplace, so I'm translating it to Python. The primers are aimed at an audience familiar with calculus and computer science so as not to "dumb down" any material, but I try to avoid using undefined terms or concepts.
I hope you find them helpful! If you have any issues with the code or material, feel free to leave a comment.