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Sometimes it's hard to see the wood for the trees

Clinton’s campaign: Could machine learning and Java have prevented their failure to handle big data?

Hillary Clinton’s 18-month campaign as the Democrat candidate for the presidency of the United States has been famously and resoundingly data-driven. A war room of senior analysts, mathematicians and researchers monitored, tracked and calculated every significant movement of her campaign, adjusting strategy to shift focus to different geo-political demographics. The polls gave affirmation that she was on track to win until the final hours of the campaign, but ultimately the pollsters failed to forecast the US election result correctly. A loss not just for Clinton and the Democrats, but for all those who are proponents of the value of data. Or so it is told.

Interview with Albert Bifet, co-leader at MOA

MOA: Machine learning for the Internet of Things

Machine learning may sound futuristic, but it’s not. Speech recognition systems such as Cortana or Search in e-commerce systems have already shown us the benefits and challenges that go hand in hand with these systems. In our machine learning series we will introduce you to several tools that make all this possible. Next stop: MOA, an open source software specific for machine learning/data mining on data streams in real time.

Interview with Adam Geitgey, Director of Software Engineering at Groupon

“Python is the most popular programming language today for machine learning”

Machine learning may sound futuristic, but it is not. Speech recognition systems such as Cortana or Search in e-commerce systems have already showed us the benefits and challenges that go hand in hand with these systems. In our machine learning series we introduced you to several tools that make all this possible. Now it’s time to allow software developer Adam Geitgey to talk about the ABCs of machine learning and teach you how to make use of ML.

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