Artificial intelligence encompasses multiple concepts, deep learning is a subset of machine learning, and natural language processing uses a wide range of AI algorithms to better understand language.

Machine learning helps solve that kind of problem by teaching the computer through examples how to recognize such fine distinctions in a world that doesn’t always operate according to strict rules.

Data scientists use dimensionality reduction in machine learning models to remove irrelevant features from busy datasets.

Machine learning allows computers to spot patterns, identify problems, and more. Here's what you need to know.

Learn how to build and deploy a machine-learning data model in a Java-based production environment using Weka, Docker, and REST.

A simple electric circuit learns to classify data in a creative new design that could lead to more energy-efficient machine-learning hardware.

Balancing Innovation And Empathy The takeaway is simple: Machine learning isn’t just about working smarter—it’s about making fintech better for everyone.

It may be the buzziest tech trend of the moment, but machine learning is no easy matter. Before you jump into writing machine learning algorithms, here are the basics you need to start a project.

Physicists have devised an algorithm that provides a mathematical framework for how learning works in lattices called mechanical neural networks.

Poor quality, unusable data is a burden for those at the end of the data’s journey. These are the data users who use it to build models and contribute to other profit-generating activities.