Machine Learning is everywhere. It decides which loan applications get approved, which medical scans get flagged for review, which play an NFL defense is likely to run on third and six. Existing blogs, tutorials, and courses tend to fall into one of two categories: technically impenetrable, or so simplified they are basically wrong. This site is an attempt at a third option.
If you have ever read a breathless writeup about a machine learning concept and thought something important was being left out, or tried a tutorial and felt like you needed a degree just to follow along, you are probably right on both counts. This site is for professionals who work alongside machine learning and want to actually understand it, and use that understanding to do their jobs more effectively.
I have spent 25 years working in machine learning and AI, across roles as a consultant, developer, and now product manager at a major tech company. Long enough to have made many mistakes and learned an abundance of lessons. My strong conviction is that professionals without a working understanding of machine learning become dependent on those who have one, at real cost to their teams and organizations. That conviction holds even in the age of AI.
Some posts dig into how machine learning concepts work conceptually: what a model is doing when it trains, where the uncertainty comes from, what practitioners mean when they say a model is overfitting. For those who want to go deeper, there are more technical posts as well. Some posts look at machine learning in the real world (how it is used in Formula 1 strategy, in hospital readmission prediction, in agriculture) and try to give those applications more texture than a press release usually does. And on Fridays, there is a shorter series called Because It’s Friday, which follows curiosity wherever it leads.
Where to Start with Machine Learning
If you want to understand how machine learning works at a high level, start with How Machine Learning Works: It’s Not Learning. It covers the core mechanism clearly and sets up most of what comes after.
If you want to see it in context to other related disciplines (where statistics ends and machine learning and AI begin), start with Making Sense of Data: From Statistics to AI.
If you want to see what a machine learning project looks like inside a real organization, start with Machine Learning in the NBA. It gets into how the sport actually uses machine learning, not just that it does.
If you just want something interesting to read on a Friday, start with Because It’s Friday: A Million Random Digits. It goes where you think it goes, and further.
