I'm going through Andrew Ng's lecture notes on Machine Learning & I just learnt about Generalized Linear Models there.
I want to check if I know correctly what Generalized Linear Models are.
Generally for Machine Learning problems (classification/prediction or regression) we can base our models on different distributions. GLMs give us a framework to form our models based on different distributions so that we can solve our problem(s). Depending on what distribution we model on, we'd get a different hypothesis function that will allow us to get the solution to our problem.
Is that definition accurate? Would you add anything if you were to define GLMs?
I'm just not entirely sure if I grasp everything about them that I should to proceed with the course videos.