Machines, most often computers, are given rules to follow known as algorithms. They are also given an initial set of data to explore when they first begin learning. That data is called training data.

Computers start to recognize patterns and make decisions based on algorithms and training data. Depending on the type of machine learning being used, they are also given targets to hit or they receive rewards when they make the right decision or take a positive step towards their end goal.

As they build this understanding or learn, they work through a series of steps to transform new inputs into outputs which may consist of brand-new datasets, labeled data, decisions, or even actions.

The idea is that they learn enough to operate without any human intervention. In this way they start to develop and demonstrate what we call artificial intelligence. Machine learning is one of the main ways artificial intelligence is created.

Other examples of artificial intelligence include robotics, speech recognition, and natural language generation, all of which also require some element of machine learning.

There are many different reasons to implement machine learning and ways to go about it. There are also a variety of machine learning algorithms and types and sources of training data.

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Machine Learning Overview | What is Machine Learning?