SAS Institute Of Management Studies - Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
- Offered byCoursera
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership at Coursera Overview
Duration | 4 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership at Coursera Highlights
- Earn a Certificate of completion from SAS on successful course completion
- Instructor - Eric Siegel - Founder of Predictive Analytics World and Deep Learning World, executive editor of "The Machine Learning Times", author of "Predictive Analytics"
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- Practice Quizzes
- Graded Assignments with Peer Feedback
- Graded Quizzes with Feedback
- Graded Programming Assignments
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership at Coursera Course details
- 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.
- Machine learning reinvents industries and runs the world. Harvard Business Review calls it ?the most important general-purpose technology of our era.? But while there are many how-to courses for hands-on techies, there are practically none that also serve business leaders ? a striking omission, since success with machine learning relies on a very particular business leadership practice just as much as it relies on adept number crunching. This specialization fills that gap. It empowers you to generate value with ML by ramping you up on both the tech and business sides ? both the cutting edge algorithms and the project management skills needed for successful deployment. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists with expansive, holistic coverage. 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 guides you on the end-to-end process required to successfully deploy ML so that it delivers a business impact. WHAT YOU'LL LEARN. How ML works, how to report on its ROI and predictive performance, best practices to lead an ML project, technical tips and tricks, how to avoid the major pitfalls, whether true AI is coming or is just a myth, and the risks to social justice that stem from ML.
- 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. 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."
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership at Coursera Curriculum
Week 1 - MODULE 1 - Business Applications of Machine Learning - This module dives deeply into the business applications of machine learning ? for marketing, financial services, fraud detection and more. We'll illustrate the value delivered for these domains by way of case studies and detailed examples. And we'll precisely measure the performance of the predictive models themselves, focusing on model lift, a predictive multiplier that tells you the improvement achieved by a model.
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
Week 2 - MODULE 2 - Scoping, Greenlighting, and Managing Machine Learning Initiatives - To make machine learning 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 module will demonstrate that practice, guiding you to lead the end-to-end implementation of machine learning.
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
Week 3 - MODULE 3 - The greatest technical hands-on bottleneck of a machine learning project is the preparation of the training data ? which is the raw material that predictive modeling software crunches, munches, and learns from. This module will guide you to prepare that data. Business priorities are front and center in the process, since they directly inform the data requirements, including the specific meaning of the dependent variable, which is the outcome or behavior your model will actually predict.
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"
Week 4 - MODULE 4 - The High Cost of False Promises, False Positives, and Misapplied Models - For many machine learning projects, high accuracy is unattainable ? and, besides, accuracy isn't the right metric in the first place. The first portion of this module will demonstrate how other metrics, such as the costs incurred by prediction errors, better serve to keep a machine learning project on track. Then we'll turn to the social good that can be achieved with machine learning, and we'll cover more social justice risks, including the hazards of predicting sensitive information such as pregnancy, job resignations, death, and ethnicity. We'll wrap up by examining the promise and perils of predictive policing.
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
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionLaunching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership