Without experience in ML it's often hard to know what problems are solvable, how to frame the problem, and to tell a good solution in Github from a bad one, etc.
If you want to go an applied route I'd suggest starting somewhere like Kaggle and looking through the competitions for ones vaguely similar to yours. They've done all the hard work of choosing a challenging but solvable problem, sourcing and splitting the data, and choosing a metric. You then can see what techniques actually work really well, and benchmark different approaches. Academic challenges like Imagenet or Coco are also good for this, but you'll have to work harder to find relevant resources.
Once you've done this a couple of times, you can start framing your own problems, collecting and annotating your own datasets, deploying and maintaining models.
If you want to go an applied route I'd suggest starting somewhere like Kaggle and looking through the competitions for ones vaguely similar to yours. They've done all the hard work of choosing a challenging but solvable problem, sourcing and splitting the data, and choosing a metric. You then can see what techniques actually work really well, and benchmark different approaches. Academic challenges like Imagenet or Coco are also good for this, but you'll have to work harder to find relevant resources.
Once you've done this a couple of times, you can start framing your own problems, collecting and annotating your own datasets, deploying and maintaining models.