The introduction clarifies what the authors mean. In this context "model" isn't about implementing a supervised model, it's about "modeling" your problem to build a bespoke algorithm that closely matches the problem. Unsupervised methods like clustering would probably fit in here too.

I haven't read much of this early access book yet, but I'd give the authors a lot of benefit of the doubt. Christopher Bishop wrote one of my favorite machine learning books (I read it after my graduate study in machine learning and it filled in a lott of the gaps): https://www.amazon.com/Pattern-Recognition-Learning-Informat...