When to build separate models
short answer: assess simple models first, and then build more complex ones if necessary.

For a roadmap, I'd go like this:


get familiar with the different categories of machine learning problems: classification vs regression problems. Your problem falls in the regression category (at least at first sight, you could also reframe it into a classification problem if necessary)
get familiar with the main corresponding algorithms
get familiar with the strategies to train / test a model performance
since you are explicitly asking for online resources, you can have a look at Andrew Ng's machine learning course (especially the first weeks of the course). It's not free, but you can also have a general overview buy looking for his Machine Learning Yearning Book. Another resource coming to my mind is Machine Learning Mastery website (can't remember the guy's name) where you can find some introductory ebooks to answer those questions. It's well written and can provide you a good lift


Does this help?