You should be thinking about what's most widely used. What will your coworkers be able to collaborate in? Where will you be able to get the most support (forums, tutorials, even libraries)? This should be the only thing that matters for your first language, and in this case its clearly Python.

I think people spend way too much time worrying about the 'best', which makes sense because its a lot of work to learn your first language. It gets easier to switch though so better to just jump into the easiest and most supported.

>How does information gathering differ between those in Applied ML and AI researchers (or even further, between those in Business Analytics and those in more 'AI' fields)

I had Data Elixir, will check the rest. Maybe it's time to trim some of the other newsletters that were probably 'influencers' trying to get easy news items off of ChatGPT.

Curious though, do you get these newsletters for general ML news, and focus on industry specifics for use cases? Or try to keep up with research papers in your area?

1. Has anyone ever seen SMOTE give good results in real world data??
2. Depends what the 500 features are, you could very well benefit from dimension reduction, or at least pruning some features, if they are not all equally useful. That is a separate topic though
3. Lot of work to create fake data when he already has that amount

Playing with the loss functions/metrics is probably the best way to go as you ( u/Far-Butterscotch-436 ) pointed out.