Machine Learning using Spark and R

R is ubiquitous in the data science community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory caching) makes it ideal […]
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Assumptions Can Ruin Your K-Means Clusters

Clustering is one of the most powerful and widely used of the machine learning techniques. It’s very seductive. Throw some data into the algorithm and let it discover hitherto unknown relationships and patterns. K-means is the most popular of all the cluster algorithms. It’s easy to understand—and therefore implement—so it’s available in almost all analysis […]
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5 Machine Learning Books Worth Reading

Machine learning remains a hot topic as organizations attempt to squeeze insights and competitive advantage from their data sets. I’ve selected five books that may be of interest if you are embarking on a machine learning initiative or career. Obviously, there are much more great books out there and I’d like to hear your recommendations—recommendation […]
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Tackling a new dataset

With all the excitement (hype?) surrounding machine learning, there’s a tendency for decision makers (and many analysts) to believe that insights are just waiting to spring forth from the data when they are finally released by these magical algorithms. When it’s time to sit down and actually work with a real dataset, disillusionment quickly sets […]
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How to Build a Predictive Model Using Azure Machine Learning

In this article, we’ll use Microsoft’s Azure Machine Learning (ML) service to predict breast cancer diagnoses from test data. If you don’t have an Azure account, a free trial is available. Note that, at the time of writing, ML is in preview, so the details may change. However, the basic concepts should still apply. Obtaining […]
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