For deep learning/NNs, Google Colab/Kaggle notebooks are where most people start. There are lots of cloud providers that are harder to set up on but might be cheaper/faster for a particular project, but while Colab is limiting in some ways it's dead simple to use so you can focus on learning data science.

There's a lot of talk about large models atm, but when you're learning you should probably stick to smaller models because the training time drastically reduces how fast you can iterate, and most jobs aren't NLP based anyway.

Remember also that 99% of problems classical models are faster and just as good or even better than neural nets. Of course if you're interested in neural nets as a hobby, go for it, they're truly fascinating and you can do some amazing things just in Colab, but they still aren't the best option for most real world problems.

If you’re just getting into the field, by far the easiest thing will be to start out with free Google Colab notebooks until you hit their free tier limits. Kaggle also has free GPUs to play with. When you’re starting out your bottleneck will likely not be your hardware but the learning curve of the content.

After that, use a managed service with GPUs. They will keep all of the hardware working, you just pay a monthly fee. I have no interest in diverting mental energy from learning ML to how to build a computer so that’s what I do. I use Paperspace but it has some very annoying issues so I can’t fully endorse it.

    https://coral.ai/

Or, search

    nvidia ai starter kit

For Stable Diffusion inference I recommend an nvidia 3060. If you want to train your own stable diffusion models I recommend a 3090. If you want to work with LLMs I recommend 2x3090.

I also recommend at least 64 GB of RAM, and at least 2 TB of storage. It seems like overkill at first but man do the GBs disappear like magic.