So far, we have introduced a broad cross-section of machine learning problems, techniques, and concepts. Hopefully by now, you have an idea of how to begin tackling a new and unique problem by breaking it up into its components. We have reviewed some of the essential mathematics and explored ways to visualize our designs. We can see that the same problem can have many different representations, and that each one may highlight different aspects. Before we can begin modeling, we need a well-defined objective, phrased as a specific, feasible, and meaningful question. We need to be clear how we can phrase the question in a way that a machine can understand.
The design process, although consisting of different and distinct activities, is not necessarily a linear process, but rather more of an iterative one. We cycle through each particular phase, proposing and testing ideas until we feel we can jump to the next phase. Sometimes we may jump back to a previous stage. We may sit at an equilibrium point, waiting for a particular event to occur; we may cycle through stages or go through several stages in parallel.
In the next chapter, we will begin our exploration of the practical tools that are available in the various Python libraries.
