As a professional developer I have always learned whatever language and tools they put in front of me. I got a .NET job on the strength of my experience with Java and other languages, worked mainly on Python at one place and then they put me to work in Typescript/React, etc.
I frequently use the same languages I use at work, for instance I do a lot of Javascript/React at work so if I wanted to make an SPA I would not screw around with Typescript or Vue but stick with what I am familiar with unless there is a really good reason. ("Write some boring program in an interesting language or framework" is a way to get burned out writing boring programs.)
My process for side projects involves a lot of thinking about things that are possible today that other people aren't doing so I can make a demo that people see and think "I never saw that before", so it is not a matter of "learning technology X" but rather "to accomplish A I will knock down whatever barriers are in the way which will involve selective learning about X, Y, Z, etc...
For instance I got interested in making printed red-cyan stereograms
https://en.wikipedia.org/wiki/Anaglyph_3D
because my son found some good examples, made some with a Nintendo New 3DS, proved that it worked, asked around on dpreview about getting a better camera, got
https://us.kandaovr.com/products/qoocam-ego
wrote my own image processing software in Python using PIL, made prints some that succeeded, made more that failed. Studying the failures I found out that the color management system that all the printing vloggers tell you to use gets in the way because it blends a little bit of red into the green to make the green primary a little more yellow to conform to the sRGB standard but the extra red leaks into the wrong eye causing ghose images. Fortunately I had already debugged this kind of problem w/ high color gamut monitors that do they same thing and made a test pattern which I made some prints with different color management settings until I really understood what was going on.
(Note, this is something you won't learn out of a book or reading web pages. If I search for "color management stereograms" the first relevant result I see is myself talking about these problems on HN.)
I am going through the same process with huggingface transformers, I already have a text classification demo running with older technology so I know when the model gets 55% AUC it sucks because another model already gets 72% AUC. From day one I am not thinking about "how do I train a good model?" but "how do I train a good model reliably?". I am a professional software developer, not a "data scientist" who cosplays at adding value to their organization. So even at an intro level where I barely know what I am doing, starting with the starter examples, I would feel like a failure if I just got it to work once in a Jupyter notebook, as soon as possible I try to "close the circle" and get that model running every day inside my RSS reader. A deeper understanding will come later.