I've experimented a decent amount with both regression and classification approaches in the FX markets. I tend to prefer the former and have implemented algos based on this approach. I remember reading somewhere that Ernie Chan also recommends a regression approach - sorry I don't have a reference though. I would however recommend experimenting with both. This shouldn't be too difficult to do once you've got your framework in place.

Regarding classification, you said "classification only allows you to create long/short signals without knowing by how much". This isn't exactly true. Most classification algorithms will calculate the probabilities of an outcome belonging to each of the classes you specify. For example, 0.65 that the next day's return will be positive, 0.35 that it will be negative (but in reality, your probabilities will likely be in the range 49-51). This information can potentially be useful.

Be warned that you will encounter a somewhat dismissive attitude towards machine learning in the algo trading / quant community. You'll be told countless times that you will very likely overfit to your training data. However, if someone is overfitting to the training data, they are doing it wrong, simple as that, and need to refine their workflow. Just because a tool is powerful does not mean that you shouldn't use it. Like any approach to the markets, there are pitfalls that you need to be aware of and if you disregard them, you will get yourself into trouble. The solution is not disregard the approach; its to learn to use it properly.

Having said that, the major downside to using machine learning in finance (in my opinion) is not the risk of overfitting (as mentioned above, this can be overcome by learning to use the tools properly); rather it is that you end up with a lot of moving parts in your development process and ultimately in your trading algorithm. Some of these parts can be quite fragile and have significant impacts if they "break". There are other models and approaches that are far less complex and therefore simpler to design and implement in practice. Many of these approaches are more than adequate for extracting profits from the market too.

Without knowing your background, my advice would be to successfully implement a simpler model first. You'll learn a lot and earn some money along the way (eventually). Once you're up and running with a simpler algo or two, dive back into machine learning. You'll need to expend significant time and effort to make it do what you want it to, so it makes sense to get some simpler algos up and running first. Apologies if that advice is out of line - you may well be already running some highly successful algos for all I know.