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

Eric Siegel

Instructor: Eric Siegel

13,326 already enrolled

Included with Coursera Plus

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5 modules
Gain insight into a topic and learn the fundamentals.
4.7

(149 reviews)

Beginner level

Recommended experience

Recommended experience

Beginner level

Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

5 modules
Gain insight into a topic and learn the fundamentals.
4.7

(149 reviews)

Beginner level

Recommended experience

Recommended experience

Beginner level

Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
  • About
  • Outcomes
  • Modules
  • Recommendations
  • Testimonials
  • Reviews

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”

Skills you'll gain

  • Predictive Analytics
  • Business Analytics
  • Decision Tree Learning
  • Applied Machine Learning
  • Artificial Intelligence
  • Data-Driven Decision-Making
  • Social Justice
  • Predictive Modeling
  • Business Ethics
  • Data Science
  • Data Ethics
  • Machine Learning
  • Performance Analysis

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

47 assignments¹

AI Graded see disclaimer
Taught in English

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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.
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  • Gain a foundational understanding of a subject or tool
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There are 5 modules in this 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.

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 videos4 readings1 app item1 discussion prompt

9 videos•Total 53 minutes
  • Machine learning in 20 seconds•7 minutes•Preview module
  • Specialization overview•4 minutes
  • Why this course isn't "hands-on" & why it's still good for techies anyway•8 minutes
  • What you'll learn: topics covered and learning objectives•3 minutes
  • Vendor-neutral courses with complementary demos from SAS•3 minutes
  • DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)•10 minutes
  • Deep learning: your path towards leveraging the hottest ML method•4 minutes
  • A tour of this specialization's courses•4 minutes
  • About your instructor, Eric Siegel•7 minutes
4 readings•Total 23 minutes
  • About the problem-solving challenges•2 minutes
  • The Machine Learning Glossary •10 minutes
  • One-question survey•1 minute
  • Frequently Asked Questions•10 minutes
1 app item•Total 10 minutes
  • Access SAS Viya for Learners (for optional hands-on demo)•10 minutes
1 discussion prompt•Total 3 minutes
  • How do you plan to use machine learning?•3 minutes

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 assignments1 peer review2 discussion prompts

13 videos•Total 79 minutes
  • Predicting the president: two common misconceptions about forecasting•8 minutes•Preview module
  • The Obama example: forecasting vs. predictive analytics•4 minutes
  • The full definitions of machine learning and predictive analytics•5 minutes
  • Buzzword heyday: putting big data and data science in their place•5 minutes
  • The two stages of machine learning: modeling and scoring•5 minutes
  • Targeting marketing with response modeling•5 minutes
  • The Prediction effect: A little prediction goes a long way•5 minutes
  • Targeted customer retention with churn modeling•6 minutes
  • Why targeting ads is like the movie "Groundhog Day"•6 minutes
  • Another application: financial credit risk•7 minutes
  • Myriad opportunities: the great range of application areas•7 minutes
  • "Non-predictive" applications: detection, classification, and diagnosis•5 minutes
  • Why ML is the latest evolutionary step of the Information Age•4 minutes
6 readings•Total 65 minutes
  • Nate Silver on misunderstanding election forecasts (optional)•10 minutes
  • Predictive analytics overview (optional)•25 minutes
  • Detailed profit calculations for targeted marketing (optional)•5 minutes
  • More information about named examples (optional) •5 minutes
  • Predictive analytics applications (optional)•5 minutes
  • White paper overviewing the organizational value of predictive analytics•15 minutes
15 assignments•Total 64 minutes
  • Predicting the president: two common misconceptions about forecasting•2 minutes
  • The Obama example: forecasting vs. predictive analytics•2 minutes
  • The full definitions of machine learning and predictive analytics•2 minutes
  • Buzzword heyday: putting big data and data science in their place•2 minutes
  • The two stages of machine learning: modeling and scoring•4 minutes
  • Targeting marketing with response modeling•4 minutes
  • The Prediction effect: A little prediction goes a long way•2 minutes
  • Targeted customer retention with churn modeling•4 minutes
  • Why targeting ads is like the movie "Groundhog Day"•2 minutes
  • Another application: financial credit risk•2 minutes
  • Myriad opportunities: the great range of application areas•2 minutes
  • "Non-predictive" applications: detection, classification, and diagnosis•2 minutes
  • Why ML is the latest evolutionary step of the Information Age•2 minutes
  • A question about the reading – the organizational value of predictive analytics•2 minutes
  • Module 1 Review •30 minutes
1 peer review•Total 20 minutes
  • Problem-solving challenge – an elevator pitch for an ML project•20 minutes
2 discussion prompts•Total 6 minutes
  • Your biggest surprise and most important learning from this module•3 minutes
  • Your most pressing unanswered question•3 minutes

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 assignments1 peer review1 app item2 discussion prompts

11 videos•Total 63 minutes
  • The big deal about big data•4 minutes•Preview module
  • A paradigm shift for scientific discovery: its automation•5 minutes
  • Example discoveries from data•6 minutes
  • The Data Effect: Data is always predictive•4 minutes
  • Training data -- what it looks like•6 minutes
  • Predicting with one single variable•4 minutes
  • Growing a decision tree to combine variables•6 minutes
  • More on decision trees•5 minutes
  • The light bulb puzzle•4 minutes
  • Measuring predictive performance: lift•6 minutes
  • DEMO - Training a simple decision tree model (optional)•9 minutes
1 reading•Total 5 minutes
  • How spending habits reveal debtor reliability (optional)•5 minutes
11 assignments•Total 54 minutes
  • The big deal about big data•2 minutes
  • A paradigm shift for scientific discovery: its automation•2 minutes
  • Example discoveries from data•2 minutes
  • The Data Effect: Data is always predictive•2 minutes
  • Training data -- what it looks like•4 minutes
  • Predicting with one single variable•2 minutes
  • Growing a decision tree to combine variables•2 minutes
  • More on decision trees•2 minutes
  • The light bulb puzzle•4 minutes
  • Measuring predictive performance: lift•2 minutes
  • Module 2 Review•30 minutes
1 peer review•Total 20 minutes
  • Problem-solving challenge – form a predictive model by hand•20 minutes
1 app item•Total 10 minutes
  • Access SAS Viya for Learners (for optional hands-on demo)•10 minutes
2 discussion prompts•Total 6 minutes
  • Your biggest surprise and most important learning from this module•3 minutes
  • Your most pressing unanswered question•3 minutes

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 assignments1 peer review1 app item2 discussion prompts

11 videos•Total 71 minutes
  • The principles of predictive modeling•6 minutes•Preview module
  • How can you trust a predictive model (train/test)?•5 minutes
  • More predictive modeling principles •6 minutes
  • Visually comparing modeling methods - decision boundaries•5 minutes
  • DEMO - Training and comparing multiple models (optional)•9 minutes
  • Deploying a predictive model•8 minutes
  • The profit curve of a model•7 minutes
  • Deployment results in targeting marketing and sales•6 minutes
  • Deep learning - application areas and limitations•6 minutes
  • Labeled data: a source of great power, yet a major limitation•5 minutes
  • Talking computers -- natural language processing and text analytics•4 minutes
4 readings•Total 30 minutes
  • Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)•5 minutes
  • Predictive analytics deployment and profit (optional)•5 minutes
  • More on deep learning (optional)•15 minutes
  • The difference between Watson and Siri (optional) •5 minutes
11 assignments•Total 51 minutes
  • The principles of predictive modeling•3 minutes
  • How can you trust a predictive model (train/test)?•2 minutes
  • More predictive modeling principles •2 minutes
  • Visually comparing modeling methods - decision boundaries•2 minutes
  • Deploying a predictive model•2 minutes
  • The profit curve of a model•2 minutes
  • Deployment results in targeting marketing and sales•2 minutes
  • Deep learning - application areas and limitations•2 minutes
  • Labeled data: a source of great power, yet a major limitation•2 minutes
  • Talking computers – natural language processing and text analytics•2 minutes
  • Module 3 Review•30 minutes
1 peer review•Total 25 minutes
  • Problem-solving challenge – draw a decision boundary•25 minutes
1 app item•Total 10 minutes
  • Access SAS Viya for Learners (for optional hands-on demo)•10 minutes
2 discussion prompts•Total 6 minutes
  • Your biggest surprise and most important learning from this module•3 minutes
  • Your most pressing unanswered question•3 minutes

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 assignments2 discussion prompts2 plugins

10 videos•Total 70 minutes
  • Why machine learning isn't becoming superintelligent•7 minutes•Preview module
  • Dismantling the logical fallacy that is AI•6 minutes
  • Why legitimizing AI as a field incurs great cost•6 minutes
  • Ethics overview: five ways ML threatens social justice•9 minutes
  • Blatantly discriminatory models•7 minutes
  • The trend towards discriminatory models•6 minutes
  • The argument against discriminatory models•7 minutes
  • Five myths about "evil" big data•8 minutes
  • Defending machine learning -- how it does good•6 minutes
  • Course wrap-up•3 minutes
4 readings•Total 40 minutes
  • AI is a big fat lie (optional) •10 minutes
  • AI is an ideology, not a technology (optional)•10 minutes
  • Book Review: Weapons of Math Destruction by Cathy O'Neil•15 minutes
  • Coded gaze on speech recognition (optional)•5 minutes
10 assignments•Total 59 minutes
  • Why machine learning isn't becoming superintelligent•2 minutes
  • Dismantling the logical fallacy that is AI•2 minutes
  • Why legitimizing AI as a field incurs great cost•2 minutes
  • Ethics overview: five ways ML threatens social justice•2 minutes
  • Blatantly discriminatory models•4 minutes
  • The trend towards discriminatory models•2 minutes
  • The argument against discriminatory models•8 minutes
  • Five myths about "evil" big data•5 minutes
  • Defending machine learning -- how it does good•2 minutes
  • Module 4 Review •30 minutes
2 discussion prompts•Total 15 minutes
  • Is "artificial general intelligence" a relevant concept?•10 minutes
  • What are your greatest ethical concerns about the application of machine learning?•5 minutes
2 plugins•Total 18 minutes
  • The coded gaze - TED talk by Joy Buolamwini (Video)•9 minutes
  • Power structures and computer vision - TED talk by Joseph Redmon (optional video)•9 minutes

Earn a career certificate

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Instructor

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4.9 (43 ratings)
Eric Siegel
Eric Siegel
SAS
5 Courses•16,899 learners

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Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.

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4.7

149 reviews

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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.

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A fantastic overview of Machine Learning and Predictive Analytics.

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Reviewed on Aug 25, 2020

Exceptionally delivered by a thoroughly informed expert

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Frequently asked questions

It's for both. To run a successful machine learning project, business leaders need to learn how machine learning works – even if they're not going to be doing the number crunching themselves. On the other hand, data scientists also benefit from a holistic curriculum that covers not only the core analytical methods, but contextualizes those methods in business terms. This curriculum serves both business leaders and data scientists, but it will not prepare you to be a hands-on practitioner – you'll need additional training for that. Rather, it is complementary to hands-on training, covering topics usually skipped there, including machine learning project management, how to prepare the data to serve business-level requirements, evaluation – calculating and reporting on the performance of a predictive model in business terms – and a deep dive into ethical issues, identifying risks to social justice and civil liberties that arise with a machine learning project and presenting options to avert these risks.

This curriculum is fully accessible to non-technical learners, business managers, and newcomers. No heavy math or coding is involved and no background in statistics or programming is required. The most technical course of this three-course specialization is the last one, which delves into the predictive modeling methods themselves. It does so in as revealing and concrete a manner as possible so as to remain relevant and understandable to non-technical learners.

No, 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. However, this specialization includes several illuminating software demos of machine learning in action using SAS products.

It's for both. This course focuses on commercial deployment and yet the curriculum is conceptually complete, as the instructor is a former university professor. It serves business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists. And it's 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.

When you use machine learning, you aren't just optimizing models and streamlining business. You're governing. The models you develop embody policies that determine access to opportunities and resources for many people. Building equitable algorithms is a crucial priority. Doing so is fundamental to harnessing the power of machine learning in a responsible manner. But a great challenge comes in defining and agreeing on the specific standards that qualify as equitable.

Each of the three courses of this specialization, Machine Learning for EveryoneOpens in a new tab, end with several videos covering topics in machine learning ethics. This coverage aims to move the discussion forward and to help form concrete standards. This means progressing beyond vague platitudes such as "be fair" and "use ML responsibly" and establishing specific, actionable principles. The topics covered include machine bias, discriminatory algorithms, model transparency and explainability, and the right to explanation.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

  • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

  • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policyOpens in a new tab.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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