Is anyone trying to learn Machine Learning?

Thanks for everyone's responses. If I were to try to tie the 3 answers together, it sounds like there's a natural interest in the tools of ML, but it's not always clear how it can be used to solve a problem that's actually relevant to your work.

I also assume there's the recognition that learning ML isn't exactly a weekend project, so you perhaps defer it because it's a lot of effort with unclear reward.

It sounds like two things that might help are:

1. Lots of examples of how ML can be used to solve a broad set of problems from the developer's perspective. Not just fancy self-driving cars and hot dog detectors, but IndieHacker scale problems/solutions.

2. Tutorials that help you dip your toe into the water in practical ways, e.g. incorporating it into your personal blog somehow, so that you can start the journey in a way that increases the ROI of your learning time.

Easier said than done, of course, but I think it makes some sense that these would be valuable resources for developers that are "on the fence". I would love to hear from some devs that are also "in the weeds", if there are any out there.

I've been learning Machine Learning for a few years now.
Background was enterprise Java before. (Boring, lol).

The challenge is finding a project and a dataset that holds your attention.
That's what I think schoon and rehero are talking about.

My solution was to take a cat-or-dog detector and pair it with lung-xrays and build a pnuemonia detector. That was easy to get motivated about (a relative had a pnuemonia scare) and something that has obvious value to non-technical people. Many people don't understand what ML can do for them, categorizing medical images is an easy slam dunk.

Here it is:
https://www.kaggle.com/shanecandoit/pneumonia-chest-xray

Free compute (and many datasets) are available on kaggle.com. So at least the eager to learn aren't out locked of using the powerful GPUs required to iterate on model building.

I am (sort of)... My background is a Computer Science bachelors degree, and now about 10 years as a professional (IBM, then a couple of startups).

The biggest challenge is very similar to @rehero's. Without a required reason to implement it into my work projects or side hustles, it feels more like a novelty and gets pushed down the priority list. It's not like a UI or database integration (which are required). It feels more like a "nice to have" and those almost never make the MVP cut.

A couple years in tech all self taught, I currently work as a Mobile App tester. My biggest challenge is finding a way to fully immerse myself in ML. I find that the best way for me to learn is sink or swim, trial by fire whatever you want to call it.

I have too many open on-going projects that I need to complete and ML isn't high on my list right now but it will be soon!