Machine learning focuses on getting computers to act without being explicitly programmed. The idea is to automate the building of analytical models that use algorithms to learn from data interactively.
Driverless cars are just one popular example of machine learning. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments. The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems.
This paper is based on presentations given over the last few years. Wayne Thompson, Manager of Data Science Technologies at SAS, introduces key machine learning concepts, explains the correlation between statistics and machine learning, and describes SAS® solutions that enable machine learning at scale.
