Back to The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

Learner Reviews & Feedback for The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats by SAS

4.8

stars

147 ratings

About the Course

It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections. Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning – no matter whether you’re more on the tech or the business side. 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. And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively. This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers: – How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations – A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees – Reporting on the predictive performance of machine learning and the profit it generates – What your data needs to look like before applying machine learning – Avoiding the hype and false promises of “artificial intelligence” – AI ethics: social justice concerns, such as when predictive models blatantly discriminate by protected class NO HANDS-ON AND NO HEAVY MATH. This concentrated entry-level program is totally accessible to business leaders – and yet totally 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 play a role on the business side or the technical side. 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. 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. 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 course 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....

Top reviews

Aug 19, 2020

This is such a well-rounded, beautifully executed coverage of ML for business people! I didn't know what I didn't know but now that I know I'm amazed this wasn't covered in other courses i took.

Apr 22, 2023

The best Machine Learning Course I've enrolled in the past 3 years. Got to extensively learn HOW machine learning is important in making business decision rather than just churning algorithms.

Filter by:

51 - 61 of 61 Reviews for The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

By Antonio L

•

Apr 28, 2021

Enlightening

By Jimmy V

•

Jan 27, 2021

Nice!

By Rahul A

•

Dec 7, 2020

Very good course, set the right contextual understanding. Could have been a bit shorter, esp the ethics part.

By Vikram K

•

Aug 27, 2021

Excellent Insights.

By KINKAR C

•

Aug 18, 2020

Nice overview!

By Derek T

•

Jul 1, 2022

The lectures are like a remake from Youtube videos, rather than something academically dense and deep.

The course is okay, but the way Mr. Eric used metaphors and jokes sometimes made lectures hard to understand. SAS lectures are optional, and they are just about 1-2 videos per week so don't expect you'll learn many things (barely scratching the surface).

Newbies who want a taste of ML can try this course. Those who interested more in the technical stuff shouldn't subscribe to this one as there are better courses out there.

By Eva N R

•

Nov 5, 2023

Comprehensive overview that ignites the lust for more.

By Maria J A S K

•

Mar 29, 2024

nice

By Fly B

•

Feb 7, 2021

A good course, though some parts are repetitive.

By Jorge T

•

Jan 18, 2021

Great introduction to Predictive Analytics

By Lana P

•

Nov 7, 2024

The material covered in this course is excellent. The peer assignments ruin the course for me. They take forever to be graded, grading is not consistent and no useful feedback was ever given. This means you cannot complete the course because the assignments take so long to grade.