About Eric Siegel

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the Predictive Analytics World and the Deep Learning World conference series, which have served more than 17,000 attendees since 2009, the instructor of the acclaimed course Machine Learning Leadership and Practice – End-to-End Mastery, a popular speaker who's been commissioned for 100+ keynote addresses, and executive editor of The Machine Learning Times. He authored the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sang educational songs to his students. Eric also publishes op-eds on analytics and social justice. Follow him at @predictanalytic.

Eric Siegel Posts (27)

  • Models Are Rarely Deployed: An Industry-wide Failure in Machine Learning Leadership - 17 Jan 2022
    In this article, Eric Siegel summarizes the recent KDnuggets poll results and argues that the pervasive failure of ML projects comes from a lack of prudent leadership. He also argues that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail.
  • New Poll: What Percentage of Your Machine Learning Models Have Been Deployed? - 29 Nov 2021
    Take a moment to participate in the latest KDnuggets poll and let the community know what percentage of your machine learning models have been deployed.
  • How Machine Learning Works for Social Good - 21 Nov 2020
    We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.
  • When Machine Learning Knows Too Much About You - 14 Nov 2020
    If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.
  • Six Ethical Quandaries of Predictive Policing - 06 Nov 2020
    When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.
  • Machine Learning’s Greatest Omission: Business Leadership - 15 Oct 2020
    Eric Siegel's business-oriented, vendor-neutral machine learning course is designed to fulfill vital unmet learner needs, delivering material critical for both techies and business leaders.
  • Coursera’s Machine Learning for Everyone Fulfills Unmet Training Needs - 17 Sep 2020
    Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs, for both the technology side and the business side, covering state-of-the-art techniques, business leadership best practices, and a wide range of common pitfalls and how to avoid them.
  • Seven Reasons to Take This Course Before You Go Hands-On with Machine Learning - 09 Sep 2020
    Eric Siegel's new course series on Coursera, Machine Learning for Everyone, is for any learner who wishes to participate in the business deployment of machine learning. This end-to-end, three-course series is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.
  • Predicting the President: Two Ways Election Forecasts Are Misunderstood - 27 Mar 2020
    With election cycles always seeming to be in season, predictions on outcomes remain intriguing content for the voting citizens. Misinterpretation of election forecasts also runs rampant, and can impact perceptions of candidates and those who post these predictions. A better fundamental understanding of probability can help improve our collective notion of futurism, and how we monitor elections.
  • Accuracy Fallacy: The Media’s Coverage of AI Is Bogus - 06 Dec 2019
    Such as the gross exaggerations Stanford researchers broadcasted about their infamous "AI gaydar" project, there exists a prevalent "accuracy fallacy" in relation to AI from the media. Find out more about how the press constantly misleads the public into believing that machine learning can reliably predict psychosis, heart attacks, sexuality, and much more.