They ship a network to convert landscape photos to landscape paintings, for example. You can retrain that with limited effort and knowledge by using their Collab notebook. That way, you can make an AI to convert photos of cats to photos of dogs. But you wouldn't be able to create an AI to convert between cats and rabbits, for example, because those are too different for that AI architecture to tolerate.

Similarly, they ship a classifier to detect objects of some categories based on surface texture. You can retrain that to detect different categories of things. But if someone paints a chair to look like elephant skin, the AI will be confused and short of inventing a new architecture yourself, there's nothing you can do to fix it.

Plus in any case, you will need to have large high quality datasets for your retraining. The biggest cost in building a bird classification app isn't the one ML employee that creates the final AI, it's hiring biologists to correctly tag thousands of photos to train the AI on.

That's why many AI research nowadays tries to offload the data generation to random people on the internet. For example, recaptcha, or crawling Flickr for that faces dataset.

Edit: since it was brought up in the comments, the retraining that I mentioned can be done only on parts of the model, which is then called transfer learning and can save you some computing time.

But, you'll need to determine if the first feature layers of the original model are suitable for your new classification task. Even experts sometimes guess wrongly here, so as a beginner, I'd say you can only pray and hope.