In the presentation below, Seth Juarez of DevExpress discusses architecting predictive algorithms for machine learning.
In biomedical research and development, researchers use text mining tools to extract and interpret facts, assertions, and relationships from vast amounts of published information. Mining accelerates the research process, increases discovery of novel findings, and helps companies identify potential safety issues in the drug development process. However, despite the many benefits of text mining, researchers face a number of obstacles before they even get a chance to run queries against the body of biomedical literature.
A pioneer in machine intelligence, ViSenze has launched its advanced SaaS visual search technology by enhancing it with a new Automatic Object Recognition feature. It enables detection and classification of products in images, making the process of searching using images easier and faster for end consumers.
Versium, a leading data technology company that delivers automated data technology solutions to marketing agencies and enterprises, announced the LifeData® Predictive Lead Score service. This self-service solution leverages machine learning and Versium’s proprietary LifeData® to help marketers quickly build accurate, customized predictive models, allowing them to improve marketing efficiencies and increase conversion rates.
In the past couple of years, we here at the insideBIGDATA brain trust have noticed a significant upward trend for all that is machine learning – technology, vendors, applications, tools, use cases, educational resources, and much more. Machine learning is a facilitator of both data science and big data. As a result, we’re excited to announce a new direction here at insideBIGDATA.
Above the Trend Line: machine learning industry rumor central, is a new recurring feature of insideBIGDATA. In this column, we’ll present a variety of short news items such as people movements, funding news, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.
Trying to show the data analysis package R is no more scary than Excel, John Mount of the Win-Vector blog shows a simple analysis both in Excel and in R.
Yahoo Inc. (NASDAQ: YHOO) announced the public release of the largest-ever machine learning dataset to the academic research community. With this release, the company aims to advance the field of large-scale machine learning and recommender systems, and to help level the playing field between industrial and academic research.
A recent query using Google Trends shows an interesting level of interest in machine learning over time (see figure below). There was an emergence in hype around the 2005 time-frame and led to a cooling off period, but once big data started heating up around 2010, the upward swing in interest continues until today.
I’ve been waiting for good book that introduces the concepts of data science and machine learning for a lay audience. Then I read an announcement of a new book that seemed to fill this need. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books; September 22, 2015) by University of Washington professor Pedro Domingos. My hopes were high and for the most part I think this book represents a good introduction to the area.