The “How Machine Learning Works” lecture series continues by building on top of the Bayesian classifier developed in Part 3 of the series. We’ll build an expectation-maximization (EM) algorithm that locally maximizes the likelihood function.
The “How Machine Learning Works” lecture series continues to build on Bayes rule that was taught last time. We’ll define training and testing data sets and build a Bayesian classifier.
FlexPod Select with Hadoop delivers enterprise class Hadoop with validated, pre-configured components for fast deployment, higher reliability and smoother integration with existing applications and infrastructure. These technical reference architectures optimize storage, networking, and servers with Cloudera and Hortonworks distributions of Hadoop.
The “How Machine Learning Works” lecture series continues by building on fundamental definitions of statistics. This is needed for any rigorous analysis of models or machine learning algorithms.
BloomReach engineer Srinath Sridhar walks through probability, Bayesian models and machine learning in this 5 part video series.
Enterprise data is growing rapidly – reaching multiple petabytes for many organizations. To maximize the business value of this data, enterprises need a storage infrastructure to store, manage, and retrieve a massive amounts of data. This eBook shows you how to address large content repository challenges with object storage.
In the video below, Field Cady of Think Big Analytics presents a compelling introduction to using the Python programming language for data science applications.
Heritage Provider Network (HPN) presented a $500,000 award to the winning team, POWERDOT, for the Heritage Health Prize competition powered by the Kaggle machine learning challenge platform. The competition ran from April 4, 2011 until April 4, 2013.
Information visualization is an increasingly important element of big data as it is the technology best able to convey the message emanating from the data. Here is a nice paper “Infovis and Statistical Graphics: Different Goals, Different Looks” (pdf) by Andrew Gelman (Professor of Statistics at Columbia University) and Antony Unwin that discusses the topic of information visualization.