What is Machine Learning?

Machine learning is the modern science of finding patterns in data and using them to make predictions.

It’s also the best way to exploit the opportunity presented by big data.

Mathematically, it’s the same field as pattern recognition, data mining, predictive analytics, and multivariate statistics, so we refer to the collection of techniques from all of these areas as machine learning methods. While technically part of machine learning, one can characterize the multivariate statistical techniques found in popular statistics packages such as SAS and SPSS as generally simpler models coming from an earlier era. Machine learning proper is composed of more modern techniques that yield higher predictive power.

For example: When using big data to score marketing leads, it is machine learning that most accurately predicts which leads will provide the greatest value and thus it is machine learning that informs business users and systems of the highest valued action to take.

Although business intelligence (BI) solutions often claim they can be used to find patterns and make predictions, they typically rely on manual, visual inspection of the data and, unlike machine learning, can offer no quantification of the accuracy of these predictions.

In virtually every industry, there exists opportunities for utilizing machine learning to increase revenue or minimize costs in areas critical to how that industry makes or loses money. For instance, in insurance, it offers better risk estimation; in banking, better credit/risk scoring; in healthcare, a better diagnosis; and in heavy-asset companies, better maintenance schedules.

Machine learning does not simply treat interesting but ancillary peripheral problems; it often addresses the core problem or problems of a company.

"While many companies are diving into the world of big data, few are yet able to harness the full power of machine learning to spot subtle or complex patterns in their data. Skytree excels in this area, making data scientists far more efficient in their efforts, and therefore enabling speedier development of more sophisticated analytics applications."

 

RESOURCES

Why Bigger is Better: 10 Facts from Theory to Practice

The Coming Machine Learning Wave – 4 Steps to Avoid Getting Left Behind