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Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
  • Level Foundation
  • Duration 14 hours
  • Course by SAS
  • Offered by

About

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."

Modules

Course Introduction

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

3 Assignment

  • Course overview
  • The ingredients of a machine learning application
  • Risky business: predictive analytics enacts risk management

3 Videos

  • Course overview: Launching Machine Learning
  • The ingredients of a machine learning application
  • Risky business: predictive analytics enacts risk management

2 Readings

  • The Machine Learning Glossary
  • One-question survey

Marketing and Sales Applications

    • 5 Videos
    • 1 Readings
    • 1 PeerReview
    • 5 Assignment
Show info about module content

5 Assignment

  • Response modeling to target marketing
  • Gains curves for response modeling
  • Churn modeling to target customer retention
  • Case study: targeting ads
  • Case study: product recommendations

1 Peer Review

  • Problem-solving challenge – deciding how to apply machine learning

5 Videos

  • Response modeling to target marketing
  • Gains curves for response modeling
  • Churn modeling to target customer retention
  • Case study: targeting ads
  • Case study: product recommendations

1 Readings

  • Retaining new customers, a killer app similar to churn modeling (optional)

Risky Business: Financial Apps and Fraud Detection

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

5 Assignment

  • Credit scoring
  • Five ways insurance companies use machine learning
  • Fraud detection
  • Case study: insurance fraud detection
  • Machine learning for government and healthcare

5 Videos

  • Credit scoring
  • Five ways insurance companies use machine learning
  • Fraud detection
  • Case study: insurance fraud detection
  • Machine learning for government and healthcare

2 Readings

  • More information about named examples (optional)
  • Generating compelling text with deep learning (optional)

Review

    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Review

2 Discussions

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

Leadership Process: How to Manage Machine Learning Projects

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

5 Assignment

  • Project management overview
  • The six steps for running a ML project
  • Running and iterating on the process steps
  • How long a machine learning project takes
  • Refining the prediction goal

5 Videos

  • Project management overview
  • The six steps for running a ML project
  • Running and iterating on the process steps
  • How long a machine learning project takes
  • Refining the prediction goal

1 Readings

  • ML project management pitfalls and best practices (optional)

Project Scoping and Greenlighting

    • 6 Videos
    • 6 Readings
    • 6 Assignment
Show info about module content

6 Assignment

  • Where to start -- picking your first ML project
  • Strategic objectives and key performance indicators
  • Personnel - staffing your machine learning team
  • Sourcing the staff for a machine learning project
  • Greenlighting: Internally selling a machine learning initiative
  • More tips for getting the green light

6 Videos

  • Where to start -- picking your first ML project
  • Strategic objectives and key performance indicators
  • Personnel - staffing your machine learning team
  • Sourcing the staff for a machine learning project
  • Greenlighting: Internally selling a machine learning initiative
  • More tips for getting the green light

6 Readings

  • Choosing the right analytics problem (optional)
  • Six ways to lower costs with predictive analytics (optional)
  • Counterpoint: AI success comes through growth, not labor savings (optional)
  • Top 10 roles in AI and data science (optional)
  • The analytics engineer (optional)
  • Need a data scientist? Try building a "DataScienceStein" (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 an ML project proposal

2 Discussions

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

Specialization Interlude and Summary

    • 1 Videos
Show info about module content

1 Videos

  • The most important video about ML ever, period

Targeting Prediction: The Dependent Variable

    • 5 Videos
    • 1 Readings
    • 1 PeerReview
    • 5 Assignment
Show info about module content

5 Assignment

  • Data prep for-the-win -- why it's absolutely crucial
  • Defining the dependent variable
  • Refining the predictive goal statement in detail
  • Identifying the sub-problem
  • How much data do you need, and how balanced?

1 Peer Review

  • Problem-solving challenge – defining a predictive goal statement

5 Videos

  • Data prep for-the-win -- why it's absolutely crucial
  • Defining the dependent variable
  • Refining the predictive goal statement in detail
  • Identifying the sub-problem
  • How much data do you need, and how balanced?

1 Readings

  • It is a mistake to ask the wrong question (optional)

Fueling Prediction: The Independent Variables

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

9 Assignment

  • A flash from the past: independent variables
  • Behavioral versus demographic data
  • Derived variables
  • Five colorful examples of behavioral data for workforce analytics
  • The predictive value of social media data
  • More social data: population trends and interpreting sentiment
  • Merging in other sources of data
  • Data cleansing: what kind of noise is okay?
  • Data disaster: "High school dropouts are better hires"

9 Videos

  • A flash from the past: independent variables
  • Behavioral versus demographic data
  • Derived variables
  • Five colorful examples of behavioral data for workforce analytics
  • The predictive value of social media data
  • More social data: population trends and interpreting sentiment
  • Merging in other sources of data
  • Data cleansing: what kind of noise is okay?
  • Data disaster: "High school dropouts are better hires"

1 Readings

  • It is a mistake to accept leaks from the future (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

Poor Judgement: Misjudging and Miscommunicating Predictive Performance

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

4 Assignment

  • Accuracy fallacy: orchestrating the media's bogus coverage of ML
  • More accuracy fallacies: predicting psychosis, criminality, & bestsellers
  • The cost of false positives and false negatives
  • Assigning costs: so important, yet so difficult

2 Discussions

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

4 Videos

  • Accuracy fallacy: orchestrating the media's bogus coverage of ML
  • More accuracy fallacies: predicting psychosis, criminality, & bestsellers
  • The cost of false positives and false negatives
  • Assigning costs: so important, yet so difficult

1 Readings

  • More reading related to the accuracy fallacy (optional)

Ethics: Regulating How Models Affect Lives

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

5 Assignment

  • Machine learning for social good
  • Predicting pregnancy -- and other sensitive machine inductions
  • Predatory micro-targeting
  • Predictive policing in law enforcement and national security
  • Course wrap-up

1 Discussions

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

5 Videos

  • Machine learning for social good
  • Predicting pregnancy -- and other sensitive machine inductions
  • Predatory micro-targeting
  • Predictive policing in law enforcement and national security
  • Course wrap-up

3 Readings

  • Machine learning for social good - more examples (optional)
  • Further insights on predicting sensitive attributes (optional)
  • Further analyses of predictive policing and ML’s effect on the balance of power (optional)

Review

    • 1 Assignment
Show info about module content

1 Assignment

  • Module 4 Review

Auto Summary

Embark on a journey to master the art of machine learning with the course "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership." This course is tailored for professionals aiming to bridge the gap between business strategy and technical expertise within the realm of data science and AI. Guided by industry expert Eric Siegel, this program is designed to help you lead or participate in the end-to-end implementation of machine learning projects, ensuring they are seamlessly integrated into business operations to drive value. Unlike typical machine learning courses that focus heavily on technical skills, this course emphasizes the crucial business leadership practices needed for successful deployment and operational success. Over 840 minutes of engaging content, you will learn to: - Identify and assess opportunities for machine learning across various domains such as marketing, sales, finance, insurance, and fraud detection. - Strategically plan the operational integration and deployment of machine learning projects, including staffing and data requirements. - Forecast the potential success of machine learning initiatives and effectively communicate their value to gain internal buy-in. - Manage the entire lifecycle of machine learning projects, from model generation to deployment. - Oversee data preparation with a focus on business priorities. - Evaluate predictive models in business terms, including profit and ROI. - Address ethical considerations and manage AI risks. This foundation-level course, which includes no hands-on coding or heavy math, is perfect for business leaders, decision-makers, data scientists, and college students or MBA candidates seeking to understand the business implications of machine learning. It offers a vendor-neutral approach with illuminating software demos, applicable regardless of the tools you choose to use. To ensure a comprehensive learning experience, it is recommended to first complete "The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats," the initial course in this specialization. Join this accessible and thorough program on Coursera and equip yourself with the knowledge to lead machine learning projects that deliver real business impact. Choose from flexible subscription options and start your journey towards becoming proficient in both the business and technical aspects of machine learning.

Eric Siegel
Instructor

Eric Siegel