Machine Learning
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, ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access