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Dr. Eric Siegel, Founder of Machine Learning Week, on 6 steps to usher in successful ML projects

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Manage episode 400570702 series 2986762
Inhoud geleverd door Dan Turchin. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door Dan Turchin of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.

Dr. Eric Siegel is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die," which has been used in courses at hundreds of universities, as well as "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment."

Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate *computer science* courses in ML and AI. Later, he served as a *business school* professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.

Listen and learn

  • How he’s progressed in the field of machine learning over 30 years
  • 6-step process to usher in machine learning programs from conception to deployment
  • What 3 things non-technical people in business should know about how machine learning works & delivers value
  • How to know when to use classical machine learning vs generative AI to solve a data problem
  • How to mitigate the impact of human bias in shaping AI

Resources

  continue reading

275 afleveringen

Artwork
iconDelen
 
Manage episode 400570702 series 2986762
Inhoud geleverd door Dan Turchin. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door Dan Turchin of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.

Dr. Eric Siegel is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die," which has been used in courses at hundreds of universities, as well as "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment."

Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate *computer science* courses in ML and AI. Later, he served as a *business school* professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.

Listen and learn

  • How he’s progressed in the field of machine learning over 30 years
  • 6-step process to usher in machine learning programs from conception to deployment
  • What 3 things non-technical people in business should know about how machine learning works & delivers value
  • How to know when to use classical machine learning vs generative AI to solve a data problem
  • How to mitigate the impact of human bias in shaping AI

Resources

  continue reading

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