Unlike supervised learning, unsupervised machine learning doesn’t require labeled data. It peruses through the training examples and divides them into clusters based on their shared characteristics.

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.

Semi-supervised learning: the best of both worlds When to use supervised vs unsupervised learning What is supervised learning? Combined with big data, this machine learning technique has the power to ...

The first two described supervised and unsupervised learning and gave examples of business applications for those two. This article will discuss semi-supervised, or hybrid, learning.

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

Unlike supervised methods that rely on known examples of threats, unsupervised algorithms learn what "normal" looks like from the vast majority of legitimate data.

Artificial intelligence (AI) and machine learning (ML) are transforming our world. When it comes to these concepts there are important differences between supervised and unsupervised learning ...

If there are examples left but no attributes, then that means these examples have the same classification (which could be due to noise, i.e. errors in the examples). In this case the most popular ...

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow Despite the success of supervised machine learning and ...