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.

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

Your phone, for example, can tell if the picture you’ve just taken is food, a face, or your pet because it was trained to recognize these different subjects using a supervised learning paradigm.

In unsupervised machine learning, the examples aren’t labeled. The AI has to classify and organize the examples based on common characteristics. Stop signs, for example, are red with white ...

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

1. Demand Prediction Engine: A Technological Leap from "Passive Response" to "Active Anticipation" ...

The core value of unsupervised learning lies in its ability for data-driven exploration, making it particularly suitable for ...

Unsupervised learning is a type of machine learning algorithm that is becoming more popular as the amount of data being produced continues to increase.

The key to a better Alexa is self-learning and semi-supervised learning techniques. Here's how Amazon is working to implement them.