Introduction to supervised machine learning

A machine learning problem can be considered as a process where an unknown variable is derived from a set of known variables using a mathematical or statistical function. The difference here is that a machine learning algorithm learns the mapping function from a given dataset.

Supervised learning is a class of machine learning algorithms where a model is trained on a dataset and the outcome for each set of inputs is already known. This is known as supervised learning as the algorithm here behaves like a teacher, guiding the training process until the desired level of model performance is achieved. Supervised learning requires data that is already labeled. Supervised learning algorithms can be further classified as parametric and non-parametric algorithms. We will look at these in the following sections.

Parametric machine learning

A machine learning algorithm that simplifies the learning process by summarizing the data with a fixed...