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Machine Learning
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This Week in Data Reading: Project Estimation, Abandoning Significance, and Video Games.
This week, Rob suggests using statistics to help you plan your next project, Carly shares some surprising use cases for artificial intelligence, and Boris imagines a world without significance testing.
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This Week in Data Reading: Experimentation, Tech and the Humanities, and Eliminating Bias in Testing
This week, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
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This Week In Data Reading: MOOCs, Collusion in Artificial Intelligence, and Fake News
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
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How to Increase Retention and Revenue in 1,000 Nontrivial Steps
Yanir reflects on how data scientists at Automattic work to improve customer retention.
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Building Thousands of Reproducible ML Models with pipe, the Automattic Machine Learning Pipeline
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
Data for Breakfast 