As mentioned earlier, machine learning is all about building mathematical models to understand data. The learning aspect enters this process when we give a machine learning model the capability to adjust its internal parameters; we can tweak these parameters so that the model explains the data better. In a sense, this can be understood as the model learning from the data. Once the model has learned enough—whatever that means—we can ask it to explain newly observed data.
A typical classification process is illustrated in the following diagram:
Let's break it down step by step.
The first thing to notice ...
