I started with Andrew Ng's ML courses, which take a bottom-up approach beginning with the math. After finishing the deeplearning.ai track, I started rounding ou

I started with Andrew Ng's ML courses, which take a bottom-up approach beginning with the math. After finishing the deeplearning.ai track, I started rounding out my skillset with some data science and R programming classes, so I could be more comfortable working with unfamiliar data. The fast.ai courses are a more top-down approach to ML, and there are plenty of good reasons for taking this approach. You'll start getting practice with libraries like Tensorflow right away. However, if you have a fairly strong math background and linear algebra doesn't give you nightmares, I highly recommend the Andrew Ng courses. A deep (ha!) understanding what's going on "under the hood" in ML will help your debugging, inform your strategy, and make your code better in the long run.