Eric Siegel covers why machine learning is the most important, most potent, most screwed up, most misunderstood, and most dangerous technology. And did I mention most important?
Yup, it’s the most important – but most projects fail to deliver value. This podcast will help you:
- Make machine learning effective and valuable
- Catch common machine learning oversights
- Understand ethical pitfalls – concretely
- Sniff out all the ”artificial intelligence” malarky
This podcast is for both data scientists and business leaders of all kinds – such as executives, directors, line of business managers, and consultants – who are involved in or affected by the deployment of machine learning.
To get machine learning to work, both the tech and business sides must make an effort to reach across wide chasm.
About the host:
Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who bridges the business and tech sides of machine learning. He is the founder of the Predictive Analytics World and Deep Learning World conference series, which have served more than 17,000 attendees since 2009. As the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery”, a winner of teaching awards as a professor, and a popular speaker, Eric has given more than 110 keynote addresses. The executive editor of The Machine Learning Times, he wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been adopted for courses at hundreds of universities. 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 – including op-eds on analytics and social justice. Follow him @predictanalytic.