Comparing Supervised vs. Unsupervised Learning

Supervised learning tends to get the most publicity in discussions of artificial intelligence techniques since it’s often the last step used to create the AI models for things like image recognition, better predictions, product recommendation and lead scoring.

In contrast, unsupervised learning tends to work behind the scenes earlier in the AI development lifecycle: It is often used to set the stage for the supervised learning’s magic to unfold, much like the grunt work that allows a manager to shine.

To read the rest of this article, please navigate to Search EnterpriseAI.

About the Contributor

About the Contributor

Mike Kim is Vice President Data & Analytics and Head of AArete’s Center of Data Excellence (CODE). Mike’s over 15 years of analytics experience includes technical delivery of machine learning/predictive models, implementation of advanced database designs, custom application development, and thought leadership for various analytic strategies. Representing healthcare, entertainment, professional sports, and numerous other industries, Mike co-leads AArete’s Digital & Data Services solutions and international expansion efforts. He holds a PhD in Health Policy and Management from Johns Hopkins University, a Masters of Public Health degree and a Bachelor of Arts degree in Biology from Brown University. Mike is a published author in Chain Drug Review, American Pharmacists Association, Mass Market Retailers, Retail TouchPoints, Advertising Age, Retail IT Insights, Managed Healthcare Executive, Property Casualty 360 and Financial Executives.