Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
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Eric Siegel, scholar, consultant and event organizer, explains how, where and why predictive analytics can be used to inform more proactive, empirically-based decision making. As part of his time at Cognizant Confluence 2011, Siegel brings a lot of good points to the table here, offering insights into why predictive analytics are useful and which business practices they can be most helpful to. The idea of predictive analytics is pulled from a lot of unstructured data, AKA Big Data. It is this unstructured data that offers valuable information and learning opportunities. And as Siegel says, “There’s never enough data” when it comes to analytics.

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