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

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."

What's inside

Syllabus

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.

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.

MODULE 3 - Data Prep: Preparing the Training Data

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.

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Surveys machine learning applications in marketing, financial services, and fraud detection, providing concrete examples

Provides guidance on managing machine learning initiatives, including scoping, greenlighting, and managing projects

Focuses on data preparation, emphasizing the importance of business priorities informing the process

Examines the ethical implications of machine learning, including privacy concerns and the potential for bias

Requires prior completion of the first course in the specialization, which may limit accessibility for some learners

Lacks hands-on exercises, which may not suit learners seeking a more practical experience

Reviews summary

Highly recommended ml course for business leaders

According to students, this course is highly recommended for business leaders who want to understand how to leverage machine learning for competitive advantage. Learners say that the course is engaging, easy to understand, and provides real-world examples. They also appreciate the instructor's enthusiasm, humor, and expertise in the field.

Practical insights and real-world applications of machine learning.

"Excellent courseGood real-life examples"

"Now I really see the light, what you need to do to actually get the benefit of machine learning. This course really drives home the practicalities of machine learning and still keeps it really interesting."

"Eric Siegel is very insightful in this 3 course machine learning specialization for data oriented business leaders!"

Essential for business leaders to gain the knowledge they need for the future.

"Excellent course for beginners and non-practitioners."

"This course is increasingly essential knowledge for both business leaders who know that data must be tapped for competitive advantage and analytics professionals who need to understand how to help businesses tap that power."

"As a non technical person (ex banker) I have been struggling to understand ML and the series of courses conducted by Eric Siegel seems to be made just for us non-technical types."

Engaging and knowledgeable instructor, Eric Siegel.

"The subject matter of this course was unique and very valuable and Prof. Siegel's enthusiasm and humor makes it especially engaging. Good job!"

"Excellent course presented by a knowledgeable expert in the machine learning field. Content was well organized and included a broad range of topics with numerous use cases."

"Prof. Siegel brings relevant and relatable cases, actual hands on work in an approachable format and keen sense of humor as he lets geek flag fly high."

Some learners found the course to be too theoretical.

"The course is too theoretical. There're many repetitive lectures can be shorten"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership with these activities:

Organize and review notes, assignments, and course materials

Show steps

Stay organized and enhance your understanding by regularly reviewing and compiling your course materials.

Show steps

Establish a system for organizing your notes, assignments, and other course materials
Review your notes regularly to reinforce your understanding of key concepts
Complete all assignments on time and review your feedback
Compile your materials into a central location for easy access

Review the basics of artificial intelligence (AI) and machine learning (ML)

Show steps

Revisit the key concepts and algorithms of AI and ML to enhance your comprehension of the course material.

Browse courses on Artificial Intelligence

Show steps

Recall the fundamental principles of AI and ML
Familiarize yourself with the different types of machine learning algorithms
Understand the applications of AI and ML in various domains

Review data analysis techniques

Show steps

Refresh your understanding of the data analysis methods and techniques that serve as the foundation of this course.

Browse courses on Data Analysis

Show steps

Review the fundamentals of data analysis, including descriptive statistics, inferential statistics, and data visualization
Practice applying these techniques to real-world datasets

Five other activities

Expand to see all activities and additional details

Show all eight activities

Explore online tutorials and workshops on machine learning best practices

Show steps

Supplement your understanding of machine learning by seeking out expert guidance through reputable online resources.

Show steps

Identify reputable online platforms that offer tutorials and workshops on machine learning
Choose tutorials that align with your skill level and the specific topics covered in the course
Actively engage in the tutorials, completing exercises and taking notes

Engage in peer review and discussion of machine learning case studies

Show steps

Enhance your understanding of machine learning applications and challenges by discussing and analyzing real-world case studies with your peers.

Show steps

Identify a group of peers who are also taking the course
Select machine learning case studies that illustrate key concepts and best practices
Facilitate discussions, share insights, and provide feedback on each other's perspectives

Solve machine learning coding challenges

Show steps

Sharpen your machine learning coding skills by solving challenging problems and coding exercises.

Show steps

Identify online platforms or resources that provide machine learning coding challenges
Select challenges that align with your skill level and course content
Work through the challenges, debugging your code and optimizing your solutions
Review your solutions and identify areas for improvement

Develop a machine learning project proposal

Show steps

Apply your knowledge of machine learning to a practical problem by creating a project proposal that outlines your approach and expected outcomes.

Show steps

Identify a real-world problem that can be addressed using machine learning
Research different machine learning algorithms and techniques that are relevant to the problem
Develop a detailed project plan that includes your data collection strategy, model selection, and evaluation criteria
Present your project proposal to a peer or mentor for feedback

Contribute to an open-source machine learning project

Show steps

Gain hands-on experience and contribute to the machine learning community by participating in an open-source project.

Show steps

Identify an open-source machine learning project that aligns with your interests
Review the project's documentation and contribute to discussions
Submit a pull request with your proposed changes or contributions
Collaborate with other contributors to refine your contributions

Career center

Learners who complete Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

As a "Machine Learning Engineer," you will build, deploy, and maintain machine learning models to solve real-world business problems. This role requires a strong foundation in computer science and machine learning algorithms. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of the machine learning lifecycle, including model development, deployment, and management. By completing this course, you will gain the skills and knowledge necessary to succeed as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

"Data Scientist" is a broad career role that encompasses data collection, analysis, and interpretation. These professionals work to extract insights from large volumes of data and develop predictive models that help businesses make more informed decisions. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course is highly relevant to this field, as it provides learners with a comprehensive understanding of the business applications of machine learning. By completing this course, you will gain the necessary knowledge to develop robust machine learning models that can drive business impact.

See salaries and explore the career path for Data Scientist

AI Engineer

"AI Engineer" is a role that involves designing, developing, and deploying AI solutions. This role requires a strong foundation in computer science, mathematics, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of the machine learning lifecycle, including model development, deployment, and management. By completing this course, you will gain the skills and knowledge necessary to become a successful AI Engineer.

See salaries and explore the career path for AI Engineer

Machine Learning Researcher

"Machine Learning Researcher" is a role that involves developing new machine learning algorithms and techniques. This role requires a strong foundation in computer science, mathematics, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of the machine learning landscape and the latest advancements in the field. By completing this course, you will gain the knowledge and skills necessary to become a successful Machine Learning Researcher.

See salaries and explore the career path for Machine Learning Researcher

Data Science Manager

"Data Science Manager" is a role that involves leading and managing a team of data scientists. This role requires a strong understanding of data science principles, machine learning, and team management. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of the machine learning lifecycle, including model development, deployment, and management. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Science Manager.

See salaries and explore the career path for Data Science Manager

Business Analyst

"Business Analyst" is a role that combines business knowledge with analytical skills to identify and solve business problems. This role requires a strong understanding of business processes, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in various business domains. By completing this course, you will gain the necessary knowledge to leverage machine learning to drive business value.

See salaries and explore the career path for Business Analyst

Data Analyst

"Data Analyst" is a role that involves collecting, cleaning, and analyzing data to identify trends and patterns. This role requires a strong foundation in data analysis techniques and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning algorithms and their applications in data analysis. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Analyst.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

"Quantitative Analyst" is a role that involves using mathematical and statistical models to analyze financial data. This role requires a strong foundation in mathematics, statistics, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning algorithms and their applications in quantitative finance. By completing this course, you will gain the skills and knowledge necessary to become a successful Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Scientist (Healthcare)

"Data Scientist, Healthcare" is a role that involves applying machine learning and data analysis techniques to healthcare data. This role requires a strong understanding of healthcare principles, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in healthcare. By completing this course, you will gain the necessary knowledge to leverage machine learning to improve healthcare outcomes. You will also build a strong foundation in healthcare principles, which will be essential for success in this role.

See salaries and explore the career path for Data Scientist (Healthcare)

Product Manager

"Product Manager" is a role that involves managing the development and launch of new products. This role requires a strong understanding of market research, product development, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of the role of machine learning in product development. By completing this course, you will gain the necessary knowledge to leverage machine learning to create innovative and successful products.

See salaries and explore the career path for Product Manager

Risk Analyst

"Risk Analyst" is a role that involves assessing and managing risks. This role requires a strong understanding of risk management principles, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in risk management. By completing this course, you will gain the necessary knowledge to leverage machine learning to identify and mitigate risks.

See salaries and explore the career path for Risk Analyst

Insurance Analyst

"Insurance Analyst" is a role that involves analyzing insurance data to identify trends and patterns. This role requires a strong understanding of insurance principles, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in insurance. By completing this course, you will gain the necessary knowledge to leverage machine learning to make informed insurance decisions.

See salaries and explore the career path for Insurance Analyst

Financial Analyst

"Financial Analyst" is a role that involves analyzing financial data to identify trends and patterns. This role requires a strong understanding of financial principles, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in finance. By completing this course, you will gain the necessary knowledge to leverage machine learning to make informed financial decisions.

See salaries and explore the career path for Financial Analyst

Healthcare Analyst

"Healthcare Analyst" is a role that involves analyzing healthcare data to identify trends and patterns. This role requires a strong understanding of healthcare principles, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in healthcare. By completing this course, you will gain the necessary knowledge to leverage machine learning to improve healthcare outcomes.

See salaries and explore the career path for Healthcare Analyst

Marketing Analyst

"Marketing Analyst" is a role that involves analyzing marketing data to identify trends and patterns. This role requires a strong understanding of marketing principles, data analysis, and machine learning. The "Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership" course provides a comprehensive overview of machine learning applications in marketing. By completing this course, you will gain the necessary knowledge to leverage machine learning to drive marketing campaigns.

See salaries and explore the career path for Marketing Analyst