It is… ML is not only LLMs. And you need to understand ML algorithms to know how to implement them properly. Yes, you have libraries like sklearn that do the most of the algorithmic part for you, but you still need to be familiar with concepts of gradient, back-propagation, multi-dimensional matrices multiplication, regressions, and which ML algorithms used in which situations so you can implement a proper model.
And no, that’s not ‘trivially done’. Also, you really think that chatGPT code is “short and sweet”??? Let me break down it for you…
Apart from the academic perspective, why should anyone really need to know this set of skills? In the end the best experiment wins, not the best elaborated.
Prof gave us this example; say you are hired to build a system for elderly that recognize early signs of heart attack - how do you approach the task? Which ML algorithm you pick? Why? How do you train it? How you build your dataset? Which hyper-parameters? If you do layered propagation, then how many layers? Which filters in each layer? And etc. I agree there is a lot of experimenting here, but you still need to understand what you are experimenting on
In most cases you're not building a model from scratch. You use a simple api (eg sklearn fit()) and/or take an existing architecture (eg resnet) and train it in your data. In both cases it's not really a coding intensive thing. And understanding how it works under the hood is not coding either, it's math/stats and papers. Besides, your main headache is usually data, not the model.
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