By: Eric Siegel, Predictive Analytics World

My new course series on Coursera, Machine Learning for Everyone (free access), fulfills two different kinds of unmet learner needs. It’s a conceptually-complete, end-to-end course series – its three courses amount to the equivalent of a college or graduate-level course – that covers both the technology side and the business side. While fully accessible and understandable to business-level learners, it’s also also vital to data scientists and budding technical practitioners, since it covers:

• The state-of-the-art techniques
• The business leadership best practices
• A wide range of common pitfalls and how to avoid them

1) A comprehensive go-to for BUSINESS-SIDE learners – by covering the following:

• ML project leadership (management process)
• ML algorithms: substantive yet accessible coverage
• Data preparation

2) Need-to-knows for EVERYONE in ML – both business-side learners and technical practitioners – by also covering the following:

• ML ethics: risks to social justice, equitable models, machine bias, etc.
• Business-oriented performance metrics
• Uplift modeling (aka persuasion modeling)
• Major pitfalls, in-depth:
  • P-hacking
  • Overfitting
  • The accuracy fallacy
  • Presuming causation from correlations
  • Serious problems with hyping ML as “AI”

This checklist illustrates the unique contribution of this curriculum:

More information about “Machine Learning for Everyone”:

Brief curriculum overview (video)

Seven Reasons Budding Data Scientists Need a Machine Learning Course That’s Not Hands-On

Geek Stuns World with Machine Learning Rap Music Video

Opening video: How Machine Learning Works – in 20 Seconds

Watch 3 Videos from Coursera’s New “Machine Learning for Everyone”

About the Author

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 long-running Predictive Analytics World and the Deep Learning World conference series, which have served more than 17,000 attendees since 2009, the instructor of the end-to-end, business-oriented Coursera specialization “Machine learning for Everyone”, a popular speaker who’s been commissioned for more than 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.