After teaching hundreds of engineers learn machine learning last 5 years, a pattern becomes hard to ignore. Most people don’t struggle because machine learning is too difficult. struggle because they start in the wrong place. The usual path looks like this: Take a crash course. Import a framework. Train a model. Tune hyperparameters and move on. They start with tools. Frameworks. APIs. Pretrained models. Everything works, until it doesn’t. At first, progress feels fast. You can reproduce a tutorial in an evening. You can train a model in an afternoon. You can get something into production surprisingly quickly. And then, slowly, friction appears. A model overfits. A small data change breaks performance. A colleague asks why a method works better than another. A paper introduces a “simple” idea that somehow feels impossible to follow. At that moment, many engineers quietly conclude: “I’m not a math person.” That conclusion is wrong. What’s actually missing is structure.…