Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we ...

Supervised learning tends to get the most publicity in discussions of artificial intelligence techniques since it's often the last step used to create the AI models for things like image recognition, ...

Supervised learning is responsible for most of the AI you interact with. 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 ...

Supervised learning is a subcategory of machine learning (ML) and artificial intelligence (AI) where a computer algorithm is trained on input data that has been labeled for a particular output. The ...

This is part Three of my series based on Lomit Patel’s “Lean AI” (O’Reilly, ISBN:978-1-492-05931-8). The first two described supervised and unsupervised learning and gave examples of business ...

Semi-supervised and unsupervised learning methods seek to extract structure and predictive power from data when labelled examples are scarce or absent. Unsupervised learning targets patterns and ...

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow Despite the success of supervised machine learning and deep ...

This is part two of my series based on Lomit Patel’s “Lean AI” (O’Reilly, ISBN:978-1-492-05931-8). The first discussed business applications can benefit from supervised learning. This article will ...

The training process for artificial intelligence (AI) algorithms is designed to be largely automated innately. There are often thousands, millions or even billions of data points and the algorithms ...