As mentioned earlier, machine learning is all about building mathematical models in order 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.
This process is illustrated in the following figure:
A typical workflow to tackle machine learning problems
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...
