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Eric Siegel

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.

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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."

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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.
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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

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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
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)
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
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
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
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
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
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership.
Comprehensive overview of deep learning. It covers the mathematical foundations of deep learning, as well as the different types of deep learning models.
Classic textbook on statistical learning. It covers the mathematical foundations of machine learning, as well as the different types of machine learning algorithms. It valuable resource for anyone who wants to understand the theory behind machine learning.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It covers the mathematical foundations of machine learning, as well as the different types of machine learning algorithms.
Comprehensive introduction to reinforcement learning. It covers the mathematical foundations of reinforcement learning, as well as the different types of reinforcement learning algorithms.
Provides a comprehensive overview of machine learning for business professionals. It covers the basics of machine learning, as well as the different types of machine learning models and how they can be used to solve business problems. It also includes case studies of real-world machine learning applications.
Provides a hands-on introduction to machine learning. It covers the basics of machine learning, as well as the different types of machine learning algorithms. It also includes hands-on exercises that allow readers to practice what they have learned.
Provides a visual introduction to machine learning. It uses diagrams and illustrations to explain the different concepts and algorithms of machine learning.
Gentle introduction to deep learning for beginners. It covers the basics of deep learning, as well as the different types of deep learning models. It also includes hands-on exercises that allow readers to practice what they have learned.
Examines the rise of artificial intelligence and its impact on the global economy. It discusses the different ways that China and the United States are approaching the development of AI, and the implications of these different approaches for the future of the world.
Examines the economic impact of artificial intelligence. It discusses the different ways that AI is being used to improve productivity and create new products and services, and the implications of these changes for the future of the economy.
Provides a hands-on introduction to machine learning for people with no prior programming experience. It covers the basics of machine learning, as well as the different types of machine learning algorithms.
Explores the possible future of humanity. It discusses the potential benefits and risks of artificial intelligence, and the implications of these technologies for the future of work, society, and the human race.

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