The title touts OpenCV, but OpenCV isn't doing much here other than unnecessarily complicating things (which is bad for newbies) and doesn't show off OpenCV's unique capabilities.

In the final code sample, OpenCV is a) loading the image, which could be done with PIL and b) training the model, when the demo imports sklearn which has its own battle-tested logistic regression implementation.

There's a lot of useful things that can be done with machine learning and computer vision, but this article is a bad demo of it that won't work on any other real-world dataset and is out-of-date with more modern CV approaches. Their previous article is a good explanation of the math behind logistic regression, though: https://machinelearningmastery.com/logistic-regression-in-op...

In fact, a neural net is “just” a stack of logistic regressions if you only use sigmoid activations.

There are lots of datasets where applying multiclass LR makes sense. Image classification isn't it.