Here is a great learning resource for anyone wishing to dive into the field of machine learning – a complete class “Machine Learning” from Spring 2011 at Carnegie Mellon University. The course is taught by Tom Mitchell, Chair of the Machine Learning Department.
Big Workflow is a new industry term coined by Adaptive Computing that refers to technology that accelerates insights by more efficiently processing intense simulations and big data analysis. Big Workflow derives its name from its ability to solve big data challenges by streamlining the workflow to deliver valuable insights from massive quantities of data across multiple platforms, environments, and locations.
As the latest installment of the Big Data Use Case series here on insideBIGDATA, we offer a compelling presentation by our friend , Jeremy Carroll, Operations engineer at Pinterest. Jeremy talks about how they use HBase at massive scale at Pinterest.
“A common thread for many of this year new IBM Fellows is their commitment to developing solutions and practical applications in the field of Big Data and Analytics. IBM is a leader in the space – with 1500 Big Data and Analytics-related patents in 2013 alone, and $24 billion in investments since 2005 through both acquisitions and R&D – and these fellows maintain the drumbeat of momentum that has made IBM number one in Big Data market share for the second year running.”
If you’ve ever spent valuable billable hours time thinking about an algorithm to seek out the optimal cheeseburger, and calculate metrics like the maximal meat-to-bun ratio, then this presentation by noted data scientist Hilary Mason at the Ignite NYC event last year is for you. Hilary, a self-admitted cheeseburger lover, found some data sets in […]
This instructional video explores how to use Hadoop and the Hortonworks Data Platform to analyze sentiment data to understand how the public feels about a product launch – highlighted is the release of the film “Iron Man 3.”
Bits are bits. Whether you are searching for whales in audio clips or trying to predict hospitalization rates based on insurance claims, the process is the same: clean the data, generate features, build a model, and iterate.