I'd recommend you take a look at course.fast.ai at some point (the earlier the better, perhaps?).

I love Jeremy Howard's "top-down" approach, whole almost every other course I've learned is bottom-up. To elaborate, the fast.ai course begins with implementing a classification model and deploying it to the web, and then unpacks everything you did and why it worked. Like unpeeling an onion layer by layer.

It has the benefit of the immediate reward of seeing something work, and you can make it work for something you find useful (e.g. maybe it's useful to you to know which images on your phone have people in the foreground vs which do not?). Once you've made something useful, you can let your curiosity drive you - why did this work, how did this work, what would I have do to improve this a certain way, etc.

Consider if this approach works for you.

For a bottom-up approach (or as a follow-up deep dive into the fast.ai course), I would say the book Deep Learning by Goodfellow, Bengio, Courville is a good resource. It's available for free at deeplearningbook.org.

There are plenty more resources out there. But I tend to feel overwhelmed by this fact, more than helped.

I would just add that for a more textbook approach, consider Pattern recognition & Machine Learning by Bishop (now available as a free PDF apparently). I personally used it while studying Machine Learning, and it does a really, really good job of listing exercises (and solutions to most) for you to test your knowledge.