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Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

Approx. 14 hours to complete

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Course Summary

This course is designed for leaders who want to understand how to launch a machine learning project and drive its success. You'll learn the key concepts, strategies, and tools necessary to lead a machine learning project in your organization.

Key Learning Points

Explore the key concepts and strategies for launching a successful machine learning project
Understand how to build a team, collect and analyze data, and select the right machine learning model for your project
Learn how to navigate ethical and legal considerations when dealing with data and machine learning

Learning Outcomes

Understand the key concepts and strategies for launching a successful machine learning project
Learn how to build and manage a team that can execute a machine learning project
Navigate ethical and legal considerations when dealing with data and machine learning

Prerequisites or good to have knowledge before taking this course

Basic understanding of machine learning concepts
Familiarity with project management principles

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Video lectures

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Notable People in This Field

Founder of deeplearning.ai
Co-Director, Stanford Institute for Human-Centered Artificial Intelligence

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Description

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."

Knowledge

Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and more
Plan ML: Determine the way 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

Outline

MODULE 1 - Business Applications of Machine Learning
Course overview: Launching Machine Learning
The ingredients of a machine learning application
Risky business: predictive analytics enacts risk management
Response modeling to target marketing
Gains curves for response modeling
Churn modeling to target customer retention
Case study: targeting ads
Case study: product recommendations
Credit scoring
Five ways insurance companies use machine learning
Fraud detection
Case study: insurance fraud detection
Machine learning for government and healthcare
The Machine Learning Glossary
One-question survey
Retaining new customers, a killer app similar to churn modeling (optional)
More information about named examples (optional)
Generating compelling text with deep learning (optional)
Course overview
The ingredients of a machine learning application
Risky business: predictive analytics enacts risk management
Response modeling to target marketing
Gains curves for response modeling
Churn modeling to target customer retention
Case study: targeting ads
Case study: product recommendations
Credit scoring
Five ways insurance companies use machine learning
Fraud detection
Case study: insurance fraud detection
Machine learning for government and healthcare
Module 1 Review

MODULE 2 - Scoping, Greenlighting, and Managing Machine Learning Initiatives
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
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
The most important video about ML ever, period
ML project management pitfalls and best practices (optional)
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)
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
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
Module 2 Review

MODULE 3 - Data Prep: Preparing the Training Data
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?
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"
It is a mistake to ask the wrong question (optional)
It is a mistake to accept leaks from the future (optional)
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?
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"
Module 3 Review

MODULE 4 - The High Cost of False Promises, False Positives, and Misapplied Models
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
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
More reading related to the accuracy fallacy (optional)
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)
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
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
Module 4 Review

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Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

Machine learning runs the world. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how....

Save Course