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

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

This course is part of Machine Learning Rock Star – the End-to-End Practice Specialization

Taught in English

Eric Siegel

Instructor: Eric Siegel

12,840 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(146 reviews)

Beginner level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Participate in the deployment of machine learning

  • Identify potential machine learning deployments that will generate value for your organization

  • Report on the predictive performance of machine learning and the profit it generates

  • Understand the potential of machine learning and avoid the false promises of “artificial intelligence”

Details to know

Shareable certificate

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Assessments

47 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(146 reviews)

Beginner level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Machine Learning Rock Star – the End-to-End Practice Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 5 modules in this course

What does this course – and the overall three-course specialization – cover and why is it right for you? Find out how this unique curriculum will empower you to generate value with machine learning. This module outlines the specialization's unusually holistic coverage and its applicability for both business-level and tech-focused learners. You'll see why this integrated coverage is a valuable place to begin, as you prepare to take on the end-to-end process of deploying machine learning. This module will orient you and frame the upcoming content – as such, it has no assessments.

What's included

9 videos3 readings1 app item1 discussion prompt

This module covers the business value of machine learning, the very purpose that it serves. You'll see what kinds of business operations machine learning improves and how it improves them. And we'll lay the foundation: what the data needs to look like, what is learned from that data, and how the predictions generated by machine learning render all kinds of large-scale operations more effective.

What's included

13 videos6 readings15 quizzes1 peer review2 discussion prompts

We are up to our ears in data, but how much can this raw material really tell us? And what actually makes it predictive? This module will show you what your data needs to look like before your computer can learn from it – the particular form and format – and you'll see the kinds of fascinating and bizarre predictive insights discovered within that data. Then we'll take the first steps in forming a predictive model, a mechanism that serves to combine such insights.

What's included

11 videos1 reading11 quizzes1 peer review1 app item2 discussion prompts

And now the main event: predictive modeling. This module will show you how software automatically generates a predictive model from data and the elegant trick that's universally applied in order to verify that the model actually works. We'll visually compare and contrast popular modeling methods and demonstrate how to draw a profit curve that estimates the bottom line that will be delivered by deploying a model. Then we'll take a hard look at both the potential and limits of machine learning – how far advanced methods like deep learning could propel us, and yet why fundamental data requirements ultimately impose certain restrictions.

What's included

11 videos4 readings11 quizzes1 peer review1 app item2 discussion prompts

Machine learning is sometimes referred to as "artificial intelligence", but that ill-defined term overpromises and confuses just as much as it elicits excitement. The first portion of this module will clear up common myths about AI and show you its downside, the costs incurred by legitimizing AI as a field. Then we'll turn to the great ethical responsibilities you are taking on by entering the field of machine learning. You'll see five ways that machine learning threatens social justice and we'll dive more deeply into one: discriminatory models that base their decisions in part on a protected class like race, religion, or sexual orientation. But then we'll shift gears and balance this out by defending machine learning, demonstrating all the good it does in the world and holding its criticisms up to a higher standard.

What's included

10 videos4 readings10 quizzes2 discussion prompts2 plugins

Instructor

Instructor ratings
4.9 (43 ratings)
Eric Siegel
SAS
5 Courses16,014 learners

Offered by

SAS

Recommended if you're interested in Machine Learning

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4.8

146 reviews

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RA
4

Reviewed on Dec 7, 2020

RM
5

Reviewed on Apr 22, 2023

HK
5

Reviewed on Nov 27, 2020

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