Machine Learning Rock Star – the End-to-End Practice

Overview

Machine learning reinvents industries and runs the world. Harvard Business Review calls it “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 the business leadership of machine learning – a striking omission, since success with machine learning relies on a very particular project leadership practice just as much as it relies on adept number crunching.

Why cover both sides? Because both sides need to learn both sides! This includes everyone leading or participating in the deployment of ML.

Rather than a hands-on training, this Machine Learning Rock Star – the End-to-End Practice course offered by Coursera in partnership with SAS serves both business leaders and burgeoning data scientists with expansive, holistic coverage.

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.

How ML works, how to report on its ROI and predictive performance, best practices to lead an ML project, technical tips and tricks, how to avoid the major pitfalls, whether true AI is coming or is just a myth, and the risks to social justice that stem from ML.

Applied Learning Project

Problem-solving challenges: Form an elevator pitch, build a predictive model by hand in Excel or Google Sheets to visualize how it improves, and more (no exercises involve the use of ML software).