As noted previously, I've been researching machine learning (just for fun).

MITx - 6.036

I'm starting with an undergraduate course from MIT that is online for free: MITx - 6.036: Introduction to Machine Learning.

Having just completed the course, here are some observations:

• The lectures are excellent, but note that they're theory- and math-heavy (and my math was rustier than I realized)
• I'd recommend learning about NumPy prior to starting this course (especially element-wise operations and broadcasting)
• So far, there has been no need (and no benefit) to enabling GPU acceleration (and setting it up on Windows was painful, for reasons I still don't understand)
• The provided code seems to be built for a previous version of TensorFlow's Keras interface (although the updates I have needed thus far were minor)

The course provides a great introduction to:

• Classification
• Regression
• Neural networks
• Convolutional neural networks
• Recurrent neural networks
• Recommender systems
• Decision trees

Note that the course doesn't cover some of the machine learning topics I'm most interested in:

• Transformers
• Generative adversarial networks

Overall, it was a fun course and good Python practice, but I wasn't really inspired to go start a machine learning project because I'm not terribly interested in classification and regression problems at the moment.