4 years ago
Coursera’s “Machine Learning for Everyone” Fulfills Unmet Training Requirements

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.