Kaggle Learn review: there is a deep learning track and it is worth your time

Yes, Al/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessable. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry.

However, contrary to the thought pieces that tend to pop up, taking and passing a MOOC doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly). They're very good for learning an overview of the technology, but nothing beats appling the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process.

Reviewing the Keras documentation (https://keras.io) and examples (https://github.com/keras-team/keras/tree/master/examples) are honestly much better teachers of AI/ML than any MOOC, in my opinion.

I'm happy to answer questions here.

I agree with the commenter saying you need to do your own projects to understand these topics.

Our deep learning track is meant to be the fastest path to knowing enough to do your own projects. You can do the entire track, including the hands-on exercises, in a single sitting.

We won't make you an expert in an afternoon, but you'll know enough to start doing your own projects. For most people that's also the point where Deep Learning becomes fun enough that you'll find time to keep learning.

Kaggle Learn is still in a very early stage. We'll add more lessons soon. But we'll stay committed to the goal of getting you up-to-speed quickly, so you can take on your own projects.

Ananth