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.

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.

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

Machine learning has proven to be very efficient at classifying images and other unstructured data, a task that is very difficult to handle with classic rule-based software. But before machine ...

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

What semi-supervised machine learning can do In practical terms, semi-supervised learning is valuable where you have a lot of data but not all of it is organized or labeled.

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

Machine learning, a branch of artificial intelligence, allows a computer to teach itself how to solve problems by analyzing large sets of data.