The point about python is that all the machine learning libraries are just wrappers around c++ libraries so, the speed of the language is largely irrelevant

Learn python, it is all you need!

Python if you want to get actual work done.

Swift for ML isn’t a thing - it was pushed by only a couple of people for a very short period of time a while ago now, and they gave up on it pretty quickly.

>I've seen a big push from Fast.ai for Swift (they claim it's the future, etc)

You've seen some dated stuff, from before S4TF became dead in the water.

The indisputable most useful language for ML is Python. The ecosystem is by far the strongest, and the language more-or-less stays out of your way while you interact with specific libraries that do what you want. Those libraries, are written in highly optimised compiled languages like C/C++, so are extremely efficient. As long as you keep them fed, you'll see very little of the "python-slow-interpreted-bad".

Definitely Python, that's what all major companies support too. However it's not the byte code cache that makes a difference but the fact that machine learning libraries are written in C++ so you're not sacrificing performance by scripting in it.

These kind of questions are more suitable on r/learndatascience though

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.

Learn python and then a few frameworks around it. Airflow for pipelines, Pandas/Dask/Vaex/Modin[ray]/PySpark for feature engineering, and then get familiar with ML libraries like tensorflow and scipy. For everything you learn, make a quick document in something like Hugo as a cheat sheet. Keep learning and documenting and you’ll be a pretty good ML engineer in no time. And if you want an easy foray into modeling, maybe start with linear regression and move onto weak ensemble like xgboost.