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Learner Reviews & Feedback for Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership by SAS

4.9
stars
51 ratings
•
15 reviews

About the Course

Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This means that two different species must cooperate in harmony: the business leader and the quant. This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment. Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within - and successfully produces value for - business operations. If you're more a quant than a business leader, you'll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don't usually go there. But know this: The soft skills are often the hard ones. After this course, you will be able to: - Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more. - Plan ML: Determine the way in which machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there. - Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues. - Lead ML: Manage a machine learning project, from the generation of predictive models to their launch. - Prep data for ML: Oversee the data preparation, which is directly informed by business priorities. - Evaluate ML: Report on the performance of predictive models in business terms, such as profit and ROI. - Regulate ML: Manage ethical pitfalls, such as when predictive models reveal sensitive information about individuals, including whether they're pregnant, will quit their job, or may be arrested - aka AI ethics. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike by contextualizing 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. There are no exercises involving coding or the use of machine learning software. WHO IT'S FOR. This concentrated entry-level program is 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. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course. 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 curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning. VENDOR-NEUTRAL. This specialization includes illuminating software demos of machine learning in action using SAS products. 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. PREREQUISITES. Before this course, learners should take the first of this specialization's three courses, "The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats."...

Top reviews

YA
Nov 23, 2020

very good and impressive course for Machine Learning with a great teacher who make you love the Machine Learning, thank you dear master for such a great course

EQ
Sep 22, 2020

The subject matter of this course was unique and very valuable and Prof. Siegel's enthusiasm and humor makes it especially engaging. Good job!

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1 - 16 of 16 Reviews for Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

By Laxman G

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Aug 16, 2020

Gold Standard MOOC.

By Carlos P

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Aug 17, 2020

Excellent insights for deployment Machine Learning at corporations. Definitely recommend if you every intend to participate either directly or indirectly at a ML development at your company. The instructor lays out a solid plan to not let the technical enthusiasm/jargon get in the way.

By Oleg K

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Aug 25, 2020

I love the ideas of machine learning and I'd love to be involved, but I'm not sure I'm up for crunching the numbers myself. This course showed me that managers and executives have an important role and also need to know this stuff.

By Dennis N

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Sep 18, 2020

Excellent course presented by a knowledgeable expert in the machine learning field. Content was well organized and included a broad range of topics with numerous use cases. Looking forward to the next course.

By Diego B

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Aug 25, 2020

Now I really see the light, what you need to do to actually get the benefit of machine learning. This course really drives home the practicalities of machine learning and still keeps it really interesting.

By Eugene Q

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Sep 22, 2020

The subject matter of this course was unique and very valuable and Prof. Siegel's enthusiasm and humor makes it especially engaging. Good job!

By Christina M

•
Aug 24, 2020

Essential business know-how for launching a machine learning project.

By PRASANTA M

•
Sep 27, 2020

Authentic learning

By Mr. J

•
Nov 17, 2020

This course, part of the Eric Segal machine learning specialization, is a must take course. All 3 are must take. They are superbly constructed and executed. The content and format and construction of the courses is - very fine. A grreat benefit to any one taking the time to complete the courses.

By Selin N

•
Oct 14, 2020

Great!

By Andrew S

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Mar 3, 2021

This is increasingly essential knowledge for both business leaders who know that data must be tapped for competitive advantage and analytics professionals who need to understand how to help businesses tap that power. Each party needs to know a bit more - not too much, but enough - about the other's world to be effective together and deliver results. Finding that balance of how much tech and data and how much business outcome to put into a course is really difficult and I have not seen anyone strike that balance like Prof. Siegel. In addition to this unique capability Prof. Siegel brings relevant and relatable cases, actual hands on work in an approachable format and keen sense of humor as he lets geek flag fly high. Take this course.

By Yasin A

•
Nov 24, 2020

very good and impressive course for Machine Learning with a great teacher who make you love the Machine Learning, thank you dear master for such a great course

By Jimmy V

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Feb 4, 2021

Opened a whole new world for me. It broadend my horizon and I view socio economics differently.

By Danielle B

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Dec 31, 2020

Enjoyed this module as well. Insightful and informative.

By k b

•
Feb 12, 2021

Excellent course for beginners and non-practitioners.

By Jorge T

•
Jan 18, 2021

Great introduction! Thanks!

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