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.

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.

Semi-supervised learning bridges both supervised and unsupervised learning by using a small section of labeled data, together with unlabeled data, to train the model.

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

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

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

For example, self-supervised learning can be utilized in monitoring to analyze data from sensors and satellite imagery, offering insights for climate change research and natural disaster management.

Combining both unsupervised and supervised machine learning defines the future of AI-based fraud prevention and is the foundation of the top nine ways AI prevents fraud: ...