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.

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

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 ...

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.

Semi-supervised learning combines supervised and unsupervised learning for efficient data analysis. This hybrid approach enhances pattern recognition from large, mixed data sets, saving time and ...

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: ...