As with any project, you enter the process with some understanding of what you are trying to build. The better you understand this (the problem), the better you are able to solve it. 

After understanding what it is that you're trying to do, your next question (in the context of building a machine learning model) is what data do I need? This includes an exploration into what data is available and what data you may need to generate yourself. 

Once you've understood what you're trying to do and what data you need, your next question/task is to decide on what algorithm (or model) is needed. This is obviously dependent on your task and the data you have; in some instances, you may be required to create your own model, but more often than not, there will be an adequate model available for you to use, or at least an architecture you can use with your own data. The following table shows some typical computer vision tasks and their related machine learning counterparts: