A common anecdote in the programming world is painting the bike shed. It speaks of a programmer, or team of programmers, worrying about what colour a bike shed should be rather than asking important questions such as whether or not the shed can actually store bikes. Of course, the bike shed can be subbed out with a computer program which serves some purpose. In the machine learning world, you will hear an endless debate between R or Python, TensorFlow or PyTorch, books or courses, math or code first (both, remember the trinity), Spark or Hadoop, Amazon Web Services or Google Cloud Platform, VSCode or Jupyter, Nvidia or… actually there's no real alternative here. The real question you should be answering is: what allows me to build my ideas in the fastest, most reliable manner?

All of these books are 400–500 pages long, with the first two being about statistical ML and the last two being about deep learning. Grab these books and find your people. Look for places that other curious programmers are spending time. For me, that was Bradfield. The kind of person who spends 10–20 hours/week learning is exactly the kind of person I wanted to study with.