Different learning systems usually follow the same design procedure. They start by acquiring the knowledge base, selecting the relevant explanatory features from the data, going through a bunch of candidate learning algorithms while keeping an eye on the performance of each one, and finally the evaluation process, which measures how successful the training process was.
In this section, we are going to address all these different design steps in more detail:
Figure 1.11: Model learning process outline