Machine Learning

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

Machine learning is a type of artificial intelligence and it provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for data patterns. However, instead of extracting data for human comprehension as is the case in data mining applications machine learning uses that data to detect patterns in data and adjust program actions accordingly. Machine learning algorithms are often categorized as being supervised or unsupervised. Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.

Machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new but one that’s gaining fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data over and over, faster and faster is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.
• Facebook’s News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling in order to read or “like” a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analysis to identify patterns in the user’s data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly.