Understanding machine learning

Machine learning uses mathematics to help understand patterns in data and make decisions based on those patterns.

There are several different branches of machine learning, including the following:

• Supervised learning, which involves using historical data to predict new values
• Unsupervised learning, which is typically used to cluster data points together based on similarity or to spot anomalies
• Reinforcement learning and semi-supervised learning, which involve systems that iteratively experiment and adapt based on the results of their experiments.

Typically, when people mention machine learning, they are referring to supervised learning, as this is the most common application of machine learning and more specific terms are usually used for the other forms of machine learning.

We'll focus on supervised learning in this book and its two most common tasks.

Supervised learning

Supervised learning is the art of training...