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.

a learning algorithm can be thought of as searching through the space of hypotheses for a hypothesis function that works well on the training set, and also on new examples that it hasn’t seen yet to ...

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

Supervised machine learning is a branch of AI. This article covers the relevant concepts, importance in various fields, practical use in investing, and CAPTCHA applications.

Deep learning based semi-supervised learning algorithms have shown promising results in recent years. However, they are not yet practical in real semi-supervised learning scenarios, such as ...

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.

Supervised learning in ML trains algorithms with labeled data, where each data point has predefined outputs, guiding the learning process.