Most of what was listed above aren't the fundamentals which would help to properly understand what's happening with neural networks but rather completely different branches of machine learning that have little in common with neural networks. If they learn SVM, naive Bayes, and gradient boosting, then their knowledge will definitely be broader but just as shallow; using your analogy, it's not like trying to program in JS without understanding for loops but rather like trying to program in JS without understanding C, COBOL and Haskell.

I'm all for learning the fundamentals properly - but those fundamentals are going to be completely things, things like core principles of statistics (limitations of correlation, confounders, bias/variance, etc), the relevant parts of calculus and linear algebra that matter for understanding optimization, the best practices for management of data, experiments and measurement to not cheat yourself, etc - not the checklist of many different, parallel methods of machine learning like decision trees or reinforcement learning, which are both useful and interesting, but not related or required to properly apply e.g. transformer-based large language models for your task.

There’s a reason people start with YouTube tutorials (which, let’s face it, are responsible for many of us passing undergrad CS classes). They give a broad, approachable explanation of the topic.

> without understanding for loops and basic programming concepts

Depending on what you mean by “understand” I would guess most software engineers don’t “understand” for loops either. For loops are an abstraction built on the CPU instruction set provided by each programming language. We use them with the knowledge of how they behave, which is the correct level of abstraction nearly 100% of the time.

And in CS undergrads, we don’t throw people into a course on assembly first. First they learn something like Java or Python, and only later dig deeper into more fundamental concepts. That’s not an accident.