[IanCal]

I think a good start is to think about what you want to do. "Back in my day" ai was mostly academic and had more classic foundational parts with newer flashy bits. It wasn't, broadly, applicable to the real world. Some parts but not a huge amount.

Now I think you've got key parts. There's how to use recent production ready models/systems, how to train them and how to make them. Is it in a research or business context?

The field is also broad enough that any one section (text, images, probably symbols) and subsection (time series, bulk, fast online work) all have significant bodies of work behind them. My splits here will not be the best currently so I'm happy for any corrections on a useful hierarchy by the way.

Perhaps you're interested in the history and what's led up to today's work? That's more of a "brief history of time" style coverage, but illuminating.

I'm aware I've not helpfully answered, but I think the same question could have very different valid goals and wanted to bring that to the fore.