Coursera’s 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”

 
Click here for free enrollment: Machine Learning for Everyone