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The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
  • Level Foundation
  • المدة 14 ساعات hours
  • الطبع بواسطة SAS
  • Offered by

عن

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.

الوحدات

Specialization Overview

    • 9 Videos
    • 3 Readings
    • 1 Discussion
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Access SAS Viya for Learners (for optional hands-on demo)

1 Discussions

  • How do you plan to use machine learning?

9 Videos

  • Machine learning in 20 seconds
  • Specialization overview
  • Why this course isn't "hands-on" & why it's still good for techies anyway
  • What you'll learn: topics covered and learning objectives
  • Vendor-neutral courses with complementary demos from SAS
  • DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)
  • Deep learning: your path towards leveraging the hottest ML method
  • A tour of this specialization's courses
  • About your instructor, Eric Siegel

3 Readings

  • About the problem-solving challenges
  • The Machine Learning Glossary
  • One-question survey

Defining Machine Learning and Predictive Analytics

    • 4 Videos
    • 2 Readings
    • 4 Assignment
Show info about module content

4 Assignment

  • Predicting the president: two common misconceptions about forecasting
  • The Obama example: forecasting vs. predictive analytics
  • The full definitions of machine learning and predictive analytics
  • Buzzword heyday: putting big data and data science in their place

4 Videos

  • Predicting the president: two common misconceptions about forecasting
  • The Obama example: forecasting vs. predictive analytics
  • The full definitions of machine learning and predictive analytics
  • Buzzword heyday: putting big data and data science in their place

2 Readings

  • Nate Silver on misunderstanding election forecasts (optional)
  • Predictive analytics overview (optional)

The Profit of Prediction

    • 3 Videos
    • 1 Readings
    • 3 Assignment
Show info about module content

3 Assignment

  • The two stages of machine learning: modeling and scoring
  • Targeting marketing with response modeling
  • The Prediction effect: A little prediction goes a long way

3 Videos

  • The two stages of machine learning: modeling and scoring
  • Targeting marketing with response modeling
  • The Prediction effect: A little prediction goes a long way

1 Readings

  • Detailed profit calculations for targeted marketing (optional)

Applications of Machine Learning

    • 5 Videos
    • 2 Readings
    • 5 Assignment
Show info about module content

5 Assignment

  • Targeted customer retention with churn modeling
  • Why targeting ads is like the movie "Groundhog Day"
  • Another application: financial credit risk
  • Myriad opportunities: the great range of application areas
  • "Non-predictive" applications: detection, classification, and diagnosis

5 Videos

  • Targeted customer retention with churn modeling
  • Why targeting ads is like the movie "Groundhog Day"
  • Another application: financial credit risk
  • Myriad opportunities: the great range of application areas
  • "Non-predictive" applications: detection, classification, and diagnosis

2 Readings

  • More information about named examples (optional)
  • Predictive analytics applications (optional)

A Great Evolutionary Step

    • 1 Videos
    • 1 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • Why ML is the latest evolutionary step of the Information Age
  • A question about the reading – the organizational value of predictive analytics

1 Videos

  • Why ML is the latest evolutionary step of the Information Age

1 Readings

  • White paper overviewing the organizational value of predictive analytics

Review

    • 2 Discussion
    • 1 PeerReview
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Review

1 Peer Review

  • Problem-solving challenge – an elevator pitch for an ML project

2 Discussions

  • Your biggest surprise and most important learning from this module
  • Your most pressing unanswered question

Discoveries: What Data Tells Us

    • 4 Videos
    • 1 Readings
    • 4 Assignment
Show info about module content

4 Assignment

  • The big deal about big data
  • A paradigm shift for scientific discovery: its automation
  • Example discoveries from data
  • The Data Effect: Data is always predictive

4 Videos

  • The big deal about big data
  • A paradigm shift for scientific discovery: its automation
  • Example discoveries from data
  • The Data Effect: Data is always predictive

1 Readings

  • How spending habits reveal debtor reliability (optional)

Gaining Insights from Training Data

    • 2 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Training data -- what it looks like
  • Predicting with one single variable

2 Videos

  • Training data -- what it looks like
  • Predicting with one single variable

The First Steps of Predictive Modeling

    • 5 Videos
    • 1 ExternalTool
    • 4 Assignment
Show info about module content

4 Assignment

  • Growing a decision tree to combine variables
  • More on decision trees
  • The light bulb puzzle
  • Measuring predictive performance: lift

1 External Tool

  • Access SAS Viya for Learners (for optional hands-on demo)

5 Videos

  • Growing a decision tree to combine variables
  • More on decision trees
  • The light bulb puzzle
  • Measuring predictive performance: lift
  • DEMO - Training a simple decision tree model (optional)

Review

    • 2 Discussion
    • 1 PeerReview
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 2 Review

1 Peer Review

  • Problem-solving challenge – form a predictive model by hand

2 Discussions

  • Your biggest surprise and most important learning from this module
  • Your most pressing unanswered question

Predictive Modeling

    • 5 Videos
    • 1 PeerReview
    • 1 ExternalTool
    • 4 Assignment
Show info about module content

4 Assignment

  • The principles of predictive modeling
  • How can you trust a predictive model (train/test)?
  • More predictive modeling principles
  • Visually comparing modeling methods - decision boundaries

1 External Tool

  • Access SAS Viya for Learners (for optional hands-on demo)

1 Peer Review

  • Problem-solving challenge – draw a decision boundary

5 Videos

  • The principles of predictive modeling
  • How can you trust a predictive model (train/test)?
  • More predictive modeling principles
  • Visually comparing modeling methods - decision boundaries
  • DEMO - Training and comparing multiple models (optional)

Deployment: Predictive Models Take Action

    • 3 Videos
    • 2 Readings
    • 3 Assignment
Show info about module content

3 Assignment

  • Deploying a predictive model
  • The profit curve of a model
  • Deployment results in targeting marketing and sales

3 Videos

  • Deploying a predictive model
  • The profit curve of a model
  • Deployment results in targeting marketing and sales

2 Readings

  • Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)
  • Predictive analytics deployment and profit (optional)

The Best That Machine Learning Can Get: Potential and Limits

    • 3 Videos
    • 2 Readings
    • 3 Assignment
Show info about module content

3 Assignment

  • Deep learning - application areas and limitations
  • Labeled data: a source of great power, yet a major limitation
  • Talking computers – natural language processing and text analytics

3 Videos

  • Deep learning - application areas and limitations
  • Labeled data: a source of great power, yet a major limitation
  • Talking computers -- natural language processing and text analytics

2 Readings

  • More on deep learning (optional)
  • The difference between Watson and Siri (optional)

Review

    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 3 Review

2 Discussions

  • Your biggest surprise and most important learning from this module
  • Your most pressing unanswered question

Hype Versus Reality: AI and Machine Learning

    • 3 Videos
    • 2 Readings
    • 1 Discussion
    • 3 Assignment
Show info about module content

3 Assignment

  • Why machine learning isn't becoming superintelligent
  • Dismantling the logical fallacy that is AI
  • Why legitimizing AI as a field incurs great cost

1 Discussions

  • Is "artificial general intelligence" a relevant concept?

3 Videos

  • Why machine learning isn't becoming superintelligent
  • Dismantling the logical fallacy that is AI
  • Why legitimizing AI as a field incurs great cost

2 Readings

  • AI is a big fat lie (optional)
  • AI is an ideology, not a technology (optional)

Ethics: With Great Power Comes Great Responsibility

    • 7 Videos
    • 2 Readings
    • 1 Discussion
    • 2 Plugin
    • 6 Assignment
Show info about module content

6 Assignment

  • Ethics overview: five ways ML threatens social justice
  • Blatantly discriminatory models
  • The trend towards discriminatory models
  • The argument against discriminatory models
  • Five myths about "evil" big data
  • Defending machine learning -- how it does good

1 Discussions

  • What are your greatest ethical concerns about the application of machine learning?

7 Videos

  • Ethics overview: five ways ML threatens social justice
  • Blatantly discriminatory models
  • The trend towards discriminatory models
  • The argument against discriminatory models
  • Five myths about "evil" big data
  • Defending machine learning -- how it does good
  • Course wrap-up

2 Readings

  • Book Review: Weapons of Math Destruction by Cathy O'Neil
  • Coded gaze on speech recognition (optional)

Review

    • 1 Assignment
Show info about module content

1 Assignment

  • Module 4 Review

Auto Summary

Unlock the potential of machine learning with this comprehensive course designed to bridge the gap between technical and business expertise. In an era where machine learning drives key business improvements such as sales boosts, cost reductions, fraud prevention, and more, this course provides a holistic, business-oriented approach that caters to both tech enthusiasts and business leaders. Led by industry expert Eric Siegel, an award-winning former professor at Columbia University, the curriculum goes beyond traditional technical foundations to include vital business insights. You will explore how predictive analytics can enhance marketing, financial services, fraud detection, and various other operations. The course also delves into predictive modeling methods, with a focus on decision trees, and discusses the ethical implications of AI, including social justice concerns. Designed to be accessible without requiring hands-on experience or heavy math, this entry-level program is perfect for business professionals, executives, directors, line of business managers, consultants, and data scientists aiming to understand the commercial deployment of machine learning. Moreover, it serves as an excellent resource for college students and MBA candidates, offering depth and breadth equivalent to a full-semester graduate-level course. With a duration of 840 minutes and available through Coursera’s Starter subscription, this vendor-neutral course includes practical software demos using SAS products but remains universally applicable regardless of the tools you use. Gain the dual expertise needed to ensure successful machine learning deployment and drive business impact. Embark on this engaging journey to master both the technical and business dimensions of machine learning, and collaborate effectively across roles to harness its full power.

Eric Siegel
Instructor

Eric Siegel