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The Official Blog of BigML.com

Machine Learning Made Simple

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Month: December 2012

December 31, 2012 Gallery

What songs will I play most in 2013?

The Quantified Self movement is entering the realm of Big Data with the ability to access the mountains of data collected by

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December 19, 2012 Fun

Introducing: Predictive Christmas Trees

You want to add a little bit of Holiday spirit to your predictive analysis? We have just the thing for

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December 14, 2012 BigMLer / Ensembles / Machine Learning

The Rewards of Ignoring Data

Can you make smarter decisions by ignoring data?  It certainly runs counter to our mission, and sounds a little like

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December 13, 2012 Gallery

I’ll Be Home For Christmas… a Little Later.

Flying home to your loved ones this Holiday season? You’re probably one of the lucky ones who could get a

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December 11, 2012 Gallery

Can I Kick It? Predicting Kickstarter Success

Crowdfunding has recently become a legitimate avenue for getting small (and big) projects the cash they need to succeed.  However,

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December 10, 2012 API / BigML.io / BigMLer / New Features

Filtering, Filtering, and Filtering

BigML is rolling out some new API features, and many of these features can be be summed up in a

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December 7, 2012 API / BigML.io / BigMLer / Ensembles

BigMLer in da Cloud: Machine Learning made even easier

We have open-sourced BigMLer, a command line tool that will let you create predictive models much easier than ever before. BigMLer

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December 6, 2012 Big Data / Developers

Free Big Data & Cloud Computing Seminar in Barcelona

On Dec 13, 2012 BigML’s Chief Technology Officer  Hacker-at-Large Jose Antonio Ortega (better known by his initials ‘jao’) will give

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December 5, 2012 News & Events / Tutorial

Tom Dietterich to give Posner Lecture at NIPS

We are pleased to announce that BigML’s Chief Scientist Tom Dietterich has been chosen to deliver the Posner Lecture at

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December 3, 2012 Machine Learning / New Features

Predicting with My Model: Is It Safe?

“Remember that all models are wrong; the practical question is how wrong do they have to be to not be

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