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

An End-to-End Guide to Leading and Launching ML. This expansive machine learning curriculum is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.
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.

By filling that gap, this course empowers you to generate value with ML. It delivers the end-to-end expertise you need, covering both the core technology and the business-side practice.

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

NO HANDS-ON. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists with expansive, holistic coverage.

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.

WHAT YOU’LL LEARN. 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).

This specialization includes several illuminating software demos of ML in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The learnings apply, regardless of which ML software you end up choosing to work with.

In-Depth Yet Accessible
Brought to you by a veteran industry leader who won 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 ML.

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.

Course Information

Estimated Time: Approximately 3 months to complete Suggested pace of 4 hours/week

Difficulty: Beginner



Course Information

Estimated Time: Approximately 3 months to complete Suggested pace of 4 hours/week

Difficulty: Beginner