“Machine Learning is a field of computer science involving creating and continuously improving algorithms that automatically analyze data to identify patterns or predict outcomes,” says the Forrester report, A Machine Learning Primer For BT Professionals. It continues,
“There are dozens of specialized classes of algorithms that focus on specific problem domains. For example, some machine-learning algorithms are designed to analyze images or video to identify objects or predict emotional state from facial expressions. Others are used to make personalized product recommendations for customers. Search engines use machine-learning algorithms to continuously improve search results. What makes machine-learning algorithms unique is that they are designed to identify patterns or make predictions by analyzing historical data that is representative of the domain.”
“Don’t get tripped up on the “learning” part of machine learning. Learning means that the algorithms analyze sets of data to look for patterns and/or correlations that result in insights. Those insights can become deeper and more accurate as new data sets are analyzed by the algorithms. For example, a recommendation engine powered by machine learning could look at taste preferences from a number of users who liked Game of Thrones, identify a pattern among them, and then use data from a new user to anticipate whether they would like the show as well. If the prediction were wrong, the new data would be fed into the machine-learning algorithm so it could adjust, or learn, the improved pattern when making the next prediction. Broadly speaking, there are two approaches to machine learning. Both have scenarios in which they are distinctly advantageous, so understanding the difference is important when building your systems and applications.”
– from A Machine Learning Primer For BT Professionals, Mike Gualtieri and Rowan Curran, September 8, 2015
