Machine Learning Times
Machine Learning Times

Original Content

Wise Practitioner – Predictive Analytics Interview Series: Jennifer Lewis Priestley at Kennesaw State University


What Every Analytics Manger Should Know About Working With Universities. But Doesn’t.

   This is part 1 of a 5-part series on university/corporate partnerships in analytics and data science How many .edu addresses do you have in your email contacts? If you are like most Analytics Managers, the answer...

Wise Practitioner – Predictive Analytics Interview Series: Kumaran Ponnambalam at Cisco


Wise Practitioner – Predictive Analytics Interview Series: Kristen Kehrer at Data Moves Me


Wise Practitioner – Predictive Analytics Interview Series: Bas Geerdink at Aizonic


Five Reasons to Go to Machine Learning Week 2020

 When deciding on a machine learning conference, why go to Machine Learning Week 2020? This five-conference event – May 31 – June 4, 2020 at Caesars Palace, Las Vegas – delivers brand-name, cross-industry, vendor-neutral case studies purely...

Why Operationalizing Machine Learning Requires a Shrewd Business Perspective

 Originally published in Analytics Magazine For a rocket scientist, the math isn’t the hardest part. What’s hard is being so often misunderstood. The same goes for data scientists, who time and again lack the support needed to...

Sampling For Your Analysis

 So you have a mailing campaign you are about to conduct. Your goal is to secure both increased response rates and sales volume. And a customer targeting methodology is crafted. Nothing elaborate-but response and sales models will...

Accuracy Fallacy: The Media’s Coverage of AI Is Bogus

  A shorter version of this article was originally published by Scientific American. Headlines about machine learning promise godlike predictive power. Here are four examples: Newsweek: “AI Can Tell If You’re Gay: Artificial Intelligence Predicts Sexuality from...

Machine Learning and Artificial Intelligence: Not New Concepts for the Data Science Practitioner

 Economic disruption is a reality which has been a gradual development over the last several decades. Artificial intelligence (AI) has simply accelerated this process.  Virtually every industry has been impacted by AI and certainly data science is...

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