Machine learning is a broad topic with many different supporting algorithms. It is generally concerned with developing techniques that allow applications to learn without having to be explicitly programmed to solve a problem. Typically, a model is built to solve a class of problems and then is trained using sample data from the problem domain. In this chapter, we will address a few of the more common problems and models used in data science.
Many of these techniques use training data to teach a model. The data consists of various representative elements of the problem space. Once the model has been trained, it is tested and evaluated using testing data. The model is then used with input data to make predictions.
For example, the purchases made by customers of a store can be used to train a model. Subsequently, predictions can be made about customers with similar characteristics. Due to the ability to predict customer behavior, it is possible to offer special deals...