Description: In this continuation of our July 2013 article we explore an algorithm for evaluating Hidden Markov Models in detail. A preferred method in speech recognition technology, Hidden Markov Models are a promising technique when dealing with data you suspect may be driven by a change in a unobservable state. The example in the article is provided in an Excel workbook for the reader to follow along with.

Description: Each month in the media we learn of another set of jobs being automated into oblivion or replaced by disruptive technologies. We see taxi drivers replaced by Uber and Lyft now, and soon by self-driving cars. An Oxford University study of 702 occupations showed insurance underwriters in 698th place, with a 99% probability of computerization. They were safer for obsolescence by automation than only four occupations such as hand sewers and telemarketers.

Description: The article introduces the concept of overfitting in predictive modeling and illustrates how it hinders our ability to predict new data well.

Description: This article sheds light on the various ways machine learning can uncover patterns within data to add unique value and insights to businesses.

Description: This article is about how insurance companies are deploying insurance technologies.

Description: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: Overview of regularization and least angle regression approaches.

Description: This paper describes techniques that can be used to access any specific piece of information in Wikipedia, such as medical information.

Description: Summary of the July, 2013 newsletter with an executive overview of each article.

Description: This article shares our experience of using the cloud to do large-scale parallel processing and discusses some background regarding the cloud.