I'm an undergrad student, and I'm nervous about picking between Tensorflow+Keras over PyTorch.
It looks like many more companies are hiring for TensorFlow, and there's a wealth of information out there on learning ML with it. In addition, it just got the 2.0 update.
But, PyTorch is preferred nearly every single time when I see the discussion come up on HN and Google searches. I'm having a hard time deciding what to dedicate my time to.
Instead, make sure to understand the math and the concepts, and then it‘s easy to translate that to an implementation.
One way of doing this (though not sufficient) is to learn both tools.
Right now the pull is away from TF (increasingly convoluted API and lots of deprecations) and towards pytorch (more support from the research community and increasing performance in production).
At the end of the course you will be able to implement almost any ML state of the art solution (classification, regression and Computer vision).
Sounds too good to be true? Jeremy have that effect, The other day in a podcast they told him Saint.
It's free btw.
YMMV