Supervised machine learning
The term machine learning typically refers to a computer program that receives input and produces output. Our goal is to train this program, also known as the model, to produce the correct output for the given input without explicitly programming it.
During this training process, the model learns the mapping between the inputs and the outputs by adjusting its internal parameters. One common way to train the model is by providing it with a set of inputs for which the correct output is known. For each of these inputs, we tell the model what the correct output is so that it can adjust, or tune itself, aiming to eventually produce the desired output for each of the given inputs. This tuning is at the heart of the learning process.
Over the years, many types of machine learning models have been developed. Each model has its own particular internal parameters that can affect the mapping between the input and the output, and the values of these parameters...
