Don't train on a laptop. Being portable and using lots of computational power are essentially opposite goals. You're not going to be able to train anything more than toys on battery, at which case if you're going to be tethered to a power cord you might as well be tethered to a desktop. You're also going to be limited in performance, due to a combination of efficiency-focussed laptop hardware as well as the thermal constraints imposed by a laptop form factor. You're far better off getting a highly portable, long battery life, but low power machine, and using cloud resources (even free ones like Colab or Paperspace) to do the heavier lifting.

If you absolutely must use a laptop because you're living out of your car or something and have nowhere to set up a desktop, then the rest depends on what you're doing:

If you're doing "deep learning" (anything involving neural networks more than a layer or two deep) you'll need a discrete GPU from NVIDIA specifically. AMD and AS support exist but are far from mature or feature complete in most frameworks. CPU need only be powerful enough to keep the GPU fed, a modern i5 or equivalent AMD will do the job, although you may find that specs with a suitable GPU aren't offered with less than an i7 / R7.

If you're not doing deep learning, you probably don't need a GPU. In that case, stick with integrated graphics and look for a higher end i7 or i9 (or equivalent AMD).

As a rule, you'll get better support on Linux than Windows or MacOS. You can skirt this in Windows via the WSL.

Finally, this post reads like you haven't even started doing whatever it is you're trying to do. I'm guessing you're a beginner just starting out, and I'd strongly advise anyone at that stage to delay purchasing hardware as long as humanly possible. Everything I noted is a generalisation, none of it specific to what you're doing because you haven't been (and likely can't be) specific to what you're doing. If you get started first using free or cheap cloud resources, you'll get a much better idea of which things you need, and which things you don't.