insideBIGDATA Guide to Machine Learning

insideBIGDATA Guide to Machine Learning

insideBIGDATA Guide to Machine Learning

As the primary facilitator of data science and big data, machine learning has garnered much interest by a broad range of industries as a way to increase value of enterprise data assets. Through techniques of supervised and unsupervised statistical learning, organizations can make important predictions and discover previously unknown knowledge to provide actionable business intelligence. In this guide, we’ll examine the principles underlying machine learning based on the R statistical environment. We’ll explore machine learning with R from the open source R perspective as well as the more robust commercial perspective using Revolution Analytics’ Revolution R Enterprise (RRE) for big data deployments.

To help our audience leverage the power of machine learning, the editors of insideBIGDATA have created this weekly article series called “The insideBIGDATA Guide to Machine Learning.” Over the next several weeks we’ll be releasing articles on:

The R statistical environment is a well accepted platform for developing machine learning solutions to business problems. R is the clear choice globally for data scientists, data analysts, and researchers alike. R is unique in terms of statistical environments in that open source R caters to practitioners exploring modestly sized data sets, performing model design, and evaluating model performance. Commercial versions of R such as RRE, take the next step forward by providing a robust, big data capable production deployment platform.

In order to help managers, administrators, and analysts build and deploy machine learning solutions, this weekly article series presents the essential strategies to help navigate today’s data science environment. If you prefer you can download the entire insideBIGDATA Guide to Machine Learning, courtesy of Revolution Analytics, by visiting the insideBIGDATA White Paper Library.

Resource Links: