To be honest, normally I'd agree with you--there's a core set of important algorithms and data structures that pretty much every programmer should be aware of and understand well enough to be able to figure out how to look for them should they need to use it. And while I normally have a visceral reaction against "XYZ every programmer should know" because it's almost invariably far more detail than what every programmer should know, your basic data structures and algorithms actually are important enough to fall into that category, and there's a reason that a course on the topic is essentially course #2 in a CS curriculum.

But after reading the table of contents here, I'm thoroughly convinced that this book is not about that. About halfway through, I gave up trying to count the algorithms it was covering because it seems like it randomly decides it wants to be an AI textbook instead. Its focus on AI is weird; if you've got time for Naïve Bayes classifiers, where's convex hull? Where's simplex [1]? Fast Fourier Transform? Union-find? Constraint satisfaction? Hell, where's quicksort?

Another harbinger of quality: there's a section on "Choosing between MD5 and SHA". If you have an entire section on that in a book published in 2023, something is wrong. Then again, an algorithms book needing to have a section on that topic is itself... o_O-worthy.

[1] Okay, there's a section on linear programming. A section. Which, based on the section headings, seems entirely focused on "here's how to use a linear programming solver" and not anything on the actual algorithm itself. My algorithms textbook has an entire chapter on simplex, with instructions on how to code the algorithm itself.