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 is that machine learning problems are always split into (at least) two distinct phases:
- A training phase, during which we aim to train a machine learning model on a set of data that we call the training dataset
- A test...
