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Data Science 101: Random Forests

Machine_Learning

The Random forests machine learning algorithm is a popular ensemble method used by many data scientists to achieve good predictive performance in the classification regime. Fully understanding the nuances of this statistical learning technique is paramount to getting the most out of this algorithm – unfortunately, this means math. The presentation below is from machine learning course CPSC 540 at The University of British Columbia,

Dr. Max Kuhn Interviewed at useR! Conference

Data Science

In the presentation below, data scientist, author (“Applied Predictive Modeling” with Kjell Johnson) and R caret package developer Max Kuhn sits down for an in-depth interview with Eduardo Arino de la Rubia sponsored by our friends over at DataScience.LA. They discuss the art and science of predictive modeling in the real world, the multifaceted and […]

The GPU Technology Conference

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March is a beautiful month to visit Silicon Valley, so why not register and book your trip now to the upcoming GPU Technology Conference (GTC) happening March 17-20, 2015 at the San Jose Convention Center. GTC is the most important event for GPU developers and computational scientists.

Data Science 101: Lessons Learned from Kaggle Competitions

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In the video presentation below, “Machine learning best practices we’ve learned from hundreds of competitions,” Ben Hamner, Chief Scientist at Kaggle, discusses some very intriguing insights into how find success in data science projects.

Interview: Szilard Pafka, Data Scientist

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The Los Angeles data science Meetup scene is booming in large part due to the efforts of a local data scientist, Szilard Pafka. In the interview below, Szilard discusses his background in the field, the genesis of his many Meetup groups, the LA tech industry, and his plans to make his Meetups even more successful.

The Rise of Data Science in the Age of Big Data Analytics

Data Science is the key to unlocking insight from Big Data: by combining computer science skills with statistical analysis and a deep understanding of the data and problem we can not only make better predictions, but also fill in gaps in our knowledge, and even find answers to questions we hadn’t even thought of yet.

The Future of AI – A Fireside Chat by Yann Lecun, Facebook

In the thought-provoking video below, Professor Yann LeCun, Director of AI Research at Facebook, sat down for a fireside chat at December 2014’s edition of Data Driven NYC to discuss deep learning and the future of artificial intelligence.

Ask a Data Scientist: Ensemble Methods

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Welcome back to our series of articles sponsored by Intel – “Ask a Data Scientist.” This week’s question is from a reader who asks about ensemble methods and how you use them.

Ask a Data Scientist: Confounding Variables

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Welcome back to our series of articles sponsored by Intel – “Ask a Data Scientist.” This week’s question is from a reader who asks for an explanation of confounding variables and why they’re important in data science projects.

Ask a Data Scientist: Data Leakage

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Welcome back to our series of articles sponsored by Intel – “Ask a Data Scientist.” Once a week you’ll see reader submitted questions of varying levels of technical detail answered by a practicing data scientist – sometimes by me and other times by an Intel data scientist. This week’s question is from a reader who asks for an explanation of data leakage.