In this article, I identify unmet learner needs that are addressed by my business-oriented machine learning course series, Machine Learning for Everyone.
Machine learning runs the world. It drives millions of business-critical decisions more effectively, guided by concrete evidence in the form of data – determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. That’s truly an upgrade to the way we do business.
Here’s the bottom line on successfully applying machine learning: You need to get a meaningful idea of how the technology works – and it’s painless because you’ll find it fascinating.
If you want to participate in the deployment of machine learning, you’ve got to take a good look under the hood. Even if you work as a business leader rather than a hands-on practitioner – even if you won’t crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you’re an executive, decision maker, or operational manager overseeing how machine learning integrates to drive better decisions, the more you know, the better.
And yet, the internal workings will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. With its impact on the world growing so quickly, it’s time to demystify the predictive power of data – and how to scientifically tap it.
That’s why I poured thousands of working hours (and 25 years of consulting and teaching experience) into a course series called Machine Learning for Everyone (now live on Coursera).
I designed it to show learners how machine learning works – in a way that’s accessible, relevant, and sometimes even entertaining. It covers the foundational underpinnings: the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which involves only straightforward arithmetic. These are things every business professional needs to know, in addition to the quants.
After all, successfully deploying machine learning relies on savvy business leadership just as much as it relies on technical skill. And for that reason, data scientists aren’t the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value.
Let me put it this way, You don’t need to learn much about the theory of spontaneous combustion in order to drive a car – HOWEVER, you do need a solid notion of how a car actually moves and operates, the way rubber maneuvers on the road. Such things are readily apparent, but only by actually observing vehicles. Likewise, make it a priority to look at how machine learning learns from data to improve operations.
Besides, cars are simple: Little explosions push them. But a computer that learns to predict? That’s a conceptual revolution. It’s not only that you won’t regret learning about it – you’ll get a kick out of it.
Do you hate complex math? That’s OK. You don’t have to follow much more than ratios and some graphical visualizations get a firm grasp of how this fascinating science works. Learning from data ain’t so mysterious. It discovers things like, If people who go to the dentist most often pay their bills on time. Such insights are then built upon to help predict customer bill payments. At its core, this technology is intuitive, powerful and awe-inspiring.
To be specific, here are some of the critical, learner-friendly topics covered by Machine Learning for Everyone, yet omitted by machine learning courses in general:
- The machine learning algorithms – in a way that’s substantive yet accessible to learners without a technical background
- The data requirements, which are heavily informed by business considerations
- Business-oriented metrics – how well it works
- The major machine learning pitfalls that derail many (or most!) ML projects
- The problems that come from hyping machine learning as “AI” and the ways in which its capabilities are often exaggerated
- Equitable machine learning: extensive coverage of risks to social justice, machine bias, discriminatory algorithms, model transparency, explainable machine learning, etc.
- Machine learning project leadership – the best practice management process
With this foundation of knowledge, you’ll be prepared to lead or participate in the value-driven use of machine learning.
To get a good sense, watch the opening video now: Machine Learning in 20 Seconds
Check out the details of the machine learning course series.
See also: Coursera’s “Machine Learning for Everyone” Fulfills Unmet Training Requirements
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.
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