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

I’m pleased to announce that, after a successful run with a batch of beta test learners, Coursera has just launched my new three-course specialization, “Machine Learning for Everyone.” There is no cost to access this program of courses.

This end-to-end course series empowers you to launch machine learning. Accessible to business-level learners and yet pertinent for techies as well, it covers both the state-of-the-art techniques and the business-side best practices.

Click here to access the complete three-course series for free

LEARNING OBJECTIVES

After these three courses, you will be able to:

• Lead ML: Manage or participate in the end-to-end implementation of machine learning
• Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more
• Greenlight ML: Forecast the effectiveness of and scope the requirements for a machine learning project and then internally sell it to gain buy-in
• Regulate ML: Manage ethical pitfalls, the risks to social justice that stem from machine learning

WATCH THE FIRST THREE VIDEOS HERE

Machine learning in 20 seconds:

 

10 intriguing questions answered during the specialization:

Why should data scientists take machine learning courses that aren’t hands-on? 

 

MORE INFORMATION ABOUT THIS COURSE SERIES

Machine learning is booming. It reinvents industries and runs the world. According to Harvard Business Review, machine learning is “the most important general-purpose technology of our era.”

But while there are so many how-to courses for hands-on techies, there are practically none that also serve business leaders – a striking omission, since success with machine learning relies on a very particular business leadership practice just as much as it relies on adept number crunching.

This specialization fills that gap. It empowers you to generate value with machine learning by ramping you up on both the technical side and the business side – both the cutting edge modeling algorithms and the project management skills needed for successful deployment.

NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists alike with expansive, holistic coverage of the state-of-the-art techniques and business-level best practices. There are no exercises involving coding or the use of machine learning software.

BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact.

IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this specialization stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.

Here’s what you will learn:

• How machine learning – aka predictive analytics – works
• How it actively improves major business operations to boost business, accumulate clicks, fight fraud, and deny deadbeats
• How to report on the increase in profit, ROI, and predictive performance it achieves
• What the data needs to looks like
• Leadership: gold standard practices for managing a machine learning project
• The technical tips and tricks – and how to avoid the most prevalent pitfalls
• Whether true artificial intelligence is coming or is just a myth
• The risks to social justice that stem from machine learning

DYNAMIC CONTENT. Across this range of topics, this specialization keeps things action-packed with case study examples, software demos, stories of poignant mistakes, and stimulating assessments.

VENDOR-NEUTRAL. This specialization includes several illuminating software demos of machine learning in action using SAS products, plus one hands-on exercise using Excel or Google Sheets. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.

WHO IT’S FOR. This concentrated entry-level program is totally accessible to business-level learners – and yet also vital to data scientists who want to secure their business relevance. It’s for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you’ll do so in the role of enterprise leader or quant. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists.

LIKE A UNIVERSITY COURSE. These three courses are also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of this specialization is equivalent to one full-semester MBA or graduate-level course.

For more information and to enroll at no cost, click here

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. Follow him at @predictanalytic.