Category: Big Data & Data Science

I, like many others, am constantly frustrated by the sensationalist use of terminology in data science. Twitter user @xaprb neatly summed up my own feelings when he tweeted When you’re fundraising, it’s AI [Artificial Intelligence]. When you’re hiring, it’s ML [Machine Learning]. When you’re implementing, it’s linear regression. When you’re debugging, it’s printf(). As someone […]

Anyone who has ever done a linear regression in R has seen an R formula. R formulae are examples of the Wilkinson notation, sometimes called the Wilkinson-Rogers notation. This same notation is used by other applications including Matlab and Octave to indicate relationships between variables. In most cases, R users have seen the Wilkinson notation […]

Choosing a machine learning (ML) platform can be a cause of considerable stress. It’s a major commitment you will have to live with for years and an unwise decision might even affect your job prospects. Fortunately, asking yourself a few fundamental questions can simply the decision considerably. Are you primarily a Windows Shop? In recent […]

Microsoft recently released a preview of a machine learning framework for .NET developers—ML.NET. I needed to perform a clustering analysis from existing data in one of my applications. This is a pretty common machine learning task, so I decided to document the basic approach in this article. We’ll use the well-worn iris data set from […]

“Big” was not always the adjective that would go with “Data” to describe its complexity and size. Towards the end of last decade, along with the popularity of Hadoop ecosystem, the term Big Data became mainstream. People started using it to classify data which was complex enough to require a cluster (like a Hadoop ecosystem) […]