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 is a branch of artificial intelligence that includes algorithms for automatically creating models from data. At a high level, there are four kinds of machine learning: supervised ...

The field of machine learning is traditionally divided into two main categories: "supervised" and "unsupervised" learning. In supervised learning, algorithms are trained on labeled data, where ...

Semi-supervised learning is a machine learning technique that trains a predictive model using supervised learning, a small set of labeled data, and a large set of unlabeled data.

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.