Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
A Refresher on Continuous Versus Discrete Input Variables
 How many times have I heard that the most...

Eric Siegel

Three Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts

  Originally published by Harvard Business Review With today’s high demand for data scientists and the high salaries that they command, it’s often not practical for companies to keep them on staff.  Instead, many organizations work to ramp up their existing staff’s analytics skills, including predictive analytics. But organizations need to proceed with caution. Predictive

Dr. Data Show Video: Why Machine Learning Is the Coolest Science

 Watch the premiere episode of The Dr. Data Show, which answers the question, “What makes machine learning the coolest science?” About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating...

Blatantly Discriminatory Machines: When Algorithms Explicitly Penalize

 Originally published in The San Francisco Chronicle (the cover article of Sunday’s “Insight” section) What if the data tells you to be racist? Without the right precautions, machine learning — the technology that drives risk-assessment in law...

AI, Machine Learning, and the Basics of Predictive Analytics for Process Management

 APQC Chair Carla O’Dell interviews Predictive Analytics Times Executive Editor and Predictive Analytics World Founder Eric Siegel about predictive analytics and machine learning’s application to process management. Dr. Siegel will be speaking at APQC’s Process & Performance...

Prediction in the Public Sector: Why the Government Needs Predictive Analytics

 Originally published by Analytics Magazine This article is excerpted from Eric Siegel’s Foreword to the recently released book, “Federal Data Science: Transforming Government and Agricultural Policy Using Artificial Intelligence,” edited by Feras A. Batarseh and Ruixin Yang....

Book Review: The Rise of Big Data Policing (Is the Data Racially Tinged?)

 Originally published by Big Think For more content on government applications of predictive analtyics, attend Predictive Analytics World for Government, Sept 18-19, 2018 in Washington, DC. As predictive analytics advances decision making across the public and private...

Twelve Hot Deep Learning Applications Featured at Deep Learning World

  For today’s leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, June 3-7, 2018.   Deep learning is white hot – both in buzz and in actual value. This...

Wise Practitioner – Predictive Analytics Interview Series: Haig R. Nalbantian at Mercer

 In anticipation of his upcoming conference co-presentation, What Millennial Employees Actually Value: Lessons from Predictive Modeling, at Predictive Analytics World for Business Las Vegas, June 3-7, 2018, we asked Haig R. Nalbantian, Senior Partner, Co-leader Mercer Workforce...

Wise Practitioner – Predictive Analytics Interview Series: Richard Boire at Environics Analytics

 In anticipation of his upcoming conference presentation, What it Means to the Data Scientist as Financial Services Faces Disruptive Times, at Predictive Analytics World for Financial in Las Vegas, June 3-7, 2018, we asked Richard Boire, Senior...

Justice Can’t Be Colorblind: How to Fight Bias with Predictive Policing

 Originally published by Scientific American Predictive policing uncovers racial inequity, which it threatens to perpetuate – but, if we turn things around, it also presents an unprecedented opportunity to advance social justice. Law enforcement’s use of predictive...

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