Machine Learning Crash Course
Looking through the topics covered, the standard AI-course caveats (<a href="https://news.ycombinator.com/item?id=16247629" rel="nofollow">https://news.ycombinator.com/item?id=16247629</a>) apply.<p>Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. 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.<p>However, contrary to the thought pieces that tend to pop up, taking and passing a crash course 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 applying the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process.<p>Reviewing the Keras documentation (<a href="https://keras.io" rel="nofollow">https://keras.io</a>) and examples (<a href="https://github.com/keras-team/keras/tree/master/examples" rel="nofollow">https://github.com/keras-team/keras/tree/master/examples</a>) are honestly much better teachers of AI/ML than any MOOC, in my opinion.<p>(Of course, Keras is now a part of TensorFlow, so there's a neat Google vertical intergration with this crash course!)