But machine learning comes in many different flavors. In this post, we will explore supervised and unsupervised learning, the two main categories of machine learning algorithms.

Here are the differences between supervised, semi-supervised, and unsupervised learning -- and how each is valuable in the enterprise.

Supervised and unsupervised learning describe two ways in which machines - algorithms - can be set loose on a data set and expected to learn something useful from it. Today, supervised machine ...

Some unsupervised algorithms have the ability to pre-process data, uncovering clusters and associations that serve as valuable inputs for a supervised model, thereby enhancing its forecasting ...

An overview of artificial intelligence (AI) and machine learning (ML) technology, including a description of how machines can be designed to learn on their own, through supervised and unsupervised ...

The three central machine-learning methodologies that programmers can use are supervised learning, unsupervised learning, and reinforcement learning.

Want to understand how machine learning impacts search? Learn how Google uses machine learning models and algorithms in search.

We’re moving on from artificial intelligence that needs training labels, called Supervised Learning, to Unsupervised Learning which is learning by finding patterns in the world.

Despite the success of supervised machine learning and deep learning, there’s a school of thought that says that unsupervised learning has even greater potential.