Machine Learning

Start with learning what is supervised classification. Yes, those differentiate between car and dog projects are the starter packs. You can't straight up jump to the problem you have. If you do, you will neither understand not be abl to debug issues you will encounter.

Next , create your training , test and evaluation data. For this, you will need some human experts to look at 1000s of images and label them as "good" or "bad".

When you have sufficient data read to go, start training an architecture that suits you. Keep tuning it and test it on test data. When you find acceptable performance, go ahead and use it in your production line.

What I said is the rough outline. You need to pad it with courses on theory in probability and calculus at the very list. There are plenty courses in Coursera to start with. And you should definitely not avoid theory and jump straight into the task cause you won't be able to identify or rectify errors you see during training especially the tricky ones.

I recommend learning from the basics . You can ask ChatGPT to make a roadmap and a planner for you . I would say if you want to get a deeper understanding of the mathematics and functioning behind how your code works then start with the math behind it. It will take longer but it will be worth it.

Have photos in two datasets, with and without the failures. Read about feature extraction, and play with https://scikit-image.org/docs/stable/auto_examples/features_detection/plot_hog.html then go into support vector machine (sklearn). Good luck!