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Eric Siegel

It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections.

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It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections.

Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning – no matter whether you’re more on the tech or the business side. After all, successfully deploying machine learning relies on savvy business leadership just as much as it relies on technical skill. And for that reason, data scientists aren't the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value.

And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively.

This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers:

– How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations

– A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees

– Reporting on the predictive performance of machine learning and the profit it generates

– What your data needs to look like before applying machine learning

– Avoiding the hype and false promises of “artificial intelligence”

– AI ethics: social justice concerns, such as when predictive models blatantly discriminate by protected class

NO HANDS-ON AND NO HEAVY MATH. This concentrated entry-level program is totally accessible to business leaders – and yet totally vital to data scientists who want to secure their business relevance. It's for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll play a role on the business side or the technical side. 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.

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 contextualizes 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.

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 course 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.

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What's inside

Syllabus

MODULE 0 - Introduction
What does this course – and the overall three-course specialization – cover and why is it right for you? Find out how this unique curriculum will empower you to generate value with machine learning. This module outlines the specialization's unusually holistic coverage and its applicability for both business-level and tech-focused learners. You'll see why this integrated coverage is a valuable place to begin, as you prepare to take on the end-to-end process of deploying machine learning. This module will orient you and frame the upcoming content – as such, it has no assessments.
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MODULE 1 - The Impact of Machine Learning
This module covers the business value of machine learning, the very purpose that it serves. You'll see what kinds of business operations machine learning improves and how it improves them. And we'll lay the foundation: what the data needs to look like, what is learned from that data, and how the predictions generated by machine learning render all kinds of large-scale operations more effective.
MODULE 2 - Data: the New Oil
We are up to our ears in data, but how much can this raw material really tell us? And what actually makes it predictive? This module will show you what your data needs to look like before your computer can learn from it – the particular form and format – and you'll see the kinds of fascinating and bizarre predictive insights discovered within that data. Then we'll take the first steps in forming a predictive model, a mechanism that serves to combine such insights.
MODULE 3 - Predictive Models: What Gets Learned from Data
And now the main event: predictive modeling. This module will show you how software automatically generates a predictive model from data and the elegant trick that's universally applied in order to verify that the model actually works. We'll visually compare and contrast popular modeling methods and demonstrate how to draw a profit curve that estimates the bottom line that will be delivered by deploying a model. Then we'll take a hard look at both the potential and limits of machine learning – how far advanced methods like deep learning could propel us, and yet why fundamental data requirements ultimately impose certain restrictions.
MODULE 4 - Industry Perspective: AI Myths and Real Ethical Risks
Machine learning is sometimes referred to as "artificial intelligence", but that ill-defined term overpromises and confuses just as much as it elicits excitement. The first portion of this module will clear up common myths about AI and show you its downside, the costs incurred by legitimizing AI as a field. Then we'll turn to the great ethical responsibilities you are taking on by entering the field of machine learning. You'll see five ways that machine learning threatens social justice and we'll dive more deeply into one: discriminatory models that base their decisions in part on a protected class like race, religion, or sexual orientation. But then we'll shift gears and balance this out by defending machine learning, demonstrating all the good it does in the world and holding its criticisms up to a higher standard.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores business and technology aspects of deploying machine learning, making it suitable for both business professionals and data scientists
Teaches software demos using SAS products, but maintains vendor-neutral curriculum allowing for universal applicability
Provides an entry point for business leaders to participate in machine learning deployment
Emphasizes practical aspects with modules on data preparation and reporting on predictive performance, catering to the needs of data scientists
Covers both technical and ethical aspects of machine learning, providing a well-rounded understanding of the field

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Reviews summary

Machine learning for everyone

Learners say this course is a largely positive introductory course to machine learning and its applications for business. The engaging assignments and clear, informative lectures make this course great for those who are new to machine learning or want to gain a broader understanding of its impact on business. Key features include an overview of machine learning concepts, examples of how machine learning is used in different industries, and a discussion of the ethical implications of machine learning.
The course provides clear, easy-to-understand explanations of machine learning concepts.
"The lectures are like a remake from Youtube videos, rather than something academically dense and deep.The course is okay, but the way Mr. Eric used metaphors and jokes sometimes made lectures hard to understand."
The course includes real-world examples of how machine learning is used in business.
"I liked the course because I learned a few things around the concerns and implications of using machine learning and developing machine learning models, to take into account the impact of the models predictive output on society at large."
The course discusses the important ethical implications of machine learning.
"Excellent. Very well done. Engaging throughout with excellent references and supplemental videos and articles. Relevant concepts and terms introduced and well-motivated. Everyone concerned about 'AI' should go through the ethics discussion. "
Eric Siegel is an engaging, knowledgeable instructor.
"Eric Siegel is very insightful in this 3 course machine learning specialization for data oriented business leaders!"
"I really loved this course. It is a must for anyone trying to understand what Machine Learning is and its application with a holistic view."

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 The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats with these activities:
Machine Learning: A Course for Non-Techies
Review the basics of machine learning before starting this course, including concepts like supervised and unsupervised learning, feature engineering, and model evaluation.
Browse courses on Machine Learning
Show steps
  • Read the course overview and syllabus.
  • Enroll in the Coursera course and watch the introductory videos.
  • Complete the pre-course quiz to assess your current knowledge.
Machine Learning with Python Tutorials
Follow online tutorials to learn the basics of machine learning using Python, a popular programming language for data science.
Show steps
  • Find a reputable online tutorial platform.
  • Choose a beginner-friendly machine learning tutorial.
  • Follow along with the tutorial and complete the exercises.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Read this book to gain a comprehensive understanding of machine learning algorithms, techniques, and tools.
Show steps
  • Read the first few chapters to get an overview of machine learning.
  • Follow along with the hands-on exercises in the book.
  • Build and train your own machine learning models.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Predictive Modeling Exercises
Practice building and evaluating predictive models, using techniques like regression analysis, classification, and decision trees.
Browse courses on Predictive Modeling
Show steps
  • Install the necessary software and libraries.
  • Load and prepare your data for modeling.
  • Build and train a predictive model using a machine learning algorithm.
  • Evaluate the performance of the model on a test set.
  • Interpret the results and make predictions.
Industry Conference
Attend an industry conference to learn about the latest trends and developments in machine learning, network with professionals in the field, and explore career opportunities.
Show steps
  • Find an industry conference that focuses on machine learning.
  • Register for the conference and attend the sessions.
  • Network with other attendees and speakers.
  • Learn about potential job openings.
Machine Learning Meetup
Attend a machine learning meetup to connect with other learners and practitioners, learn about new techniques, and share your own experiences.
Show steps
  • Find a local machine learning meetup group.
  • Attend a meetup and introduce yourself.
  • Participate in discussions and ask questions.
  • Network with other attendees.
Machine Learning Project
Build a machine learning project to apply the skills and knowledge you've gained in this course to a real-world problem.
Show steps
  • Define the problem you want to solve.
  • Collect and prepare your data.
  • Build and train a machine learning model.
  • Evaluate the performance of your model.
  • Deploy your model and make predictions.
Contribute to an Open-Source Machine Learning Project
Contribute to an open-source machine learning project to gain practical experience, learn from others, and make a meaningful impact.
Show steps
  • Find an open-source machine learning project that aligns with your interests.
  • Read the project documentation and contribute to the discussion forums.
  • Fix bugs, implement new features, or improve documentation.
  • Submit a pull request and get your changes merged.

Career center

Learners who complete The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists apply sophisticated algorithms such as decision trees to solve problems and make predictions for businesses, in domains as diverse as marketing, finance, fraud detection, and retail. This course introduces Data Scientists, the fastest growing new job category on LinkedIn, to data science at a business-oriented and accessible level, from the very basics through cutting-edge topics, so that it may help you to join the field.
Machine Learning Engineer
Machine Learning Engineers design and develop the algorithms and software that enable computers to learn without explicit programming. These algorithms can drive applications as diverse as search engines, social networks, fraud detection systems, predictive maintenance programs, and self-driving cars. Specific job functions vary widely. However, this course will familiarize you with the business context for machine learning, the types of modeling approaches there are, and the questions to ask about data, so that you can better collaborate with your business colleagues and ensure your work has a real-world impact.
Business Analyst
Business Analysts take business goals and transform them into actionable recommendations to organizations, by identifying process improvements, automation opportunities, technology solutions, and more. You can learn about the business value of machine learning and how it can improve business performance, so that you may make better recommendations to clients in various industries.
Operations Research Analyst
Operations Research Analysts improve decision-making for organizations by devising and applying analytical techniques to solve complex problems. This course introduces you to the foundational ideas and key methods of machine learning, providing a solid foundation for further study of the discipline.
Data Analyst
Data Analysts are on the front lines of extracting and communicating insights from big data. By mastering the foundational principles of data analysis and machine learning, you'll be well-positioned to enter this growing field and help businesses exploit their information assets to make better decisions.
Marketing Manager
Marketing Managers develop and execute marketing plans to achieve specific business objectives, such as increasing sales or brand awareness. This course will teach you how machine learning is reshaping marketing, through applications such as predictive analytics, customer segmentation, and personalized marketing.
Statistician
Statisticians collect, analyze, and interpret data to inform decision-making. An understanding of machine learning is increasingly important for Statisticians as it helps automate complex data analysis tasks and provides new tools for inference and prediction.
Software Engineer
Software Engineers design, develop, and maintain software systems. With the rise of AI, machine learning is becoming an integral part of software development. Thus, this course may help prepare you for this exciting new direction in the field.
Product Manager
Product Managers are responsible for the success of a product throughout its lifecycle. With machine learning rapidly transforming the design and development of new products and services, this course can help you to understand how to leverage machine learning to create innovative products that meet customer needs.
Financial Analyst
Financial Analysts make investment recommendations and provide financial advice to individuals and organizations. By understanding how machine learning is revolutionizing the financial industry, you can make better use of the latest tools and technologies to support your analysis.
Risk Manager
Risk Managers identify, assess, and mitigate risks for organizations. This course will introduce you to the role of machine learning in risk management, including its use in fraud detection, credit risk assessment, and insurance pricing.
Sales Manager
Sales Managers lead and motivate sales teams to achieve revenue goals. This course will help you to understand how machine learning can be used to improve sales performance, through applications such as lead scoring, churn prediction, and personalized marketing.
Consultant
Consultants advise clients on a wide range of business issues. By completing this course, you'll be well-positioned to advise clients on how they can use machine learning to improve their business processes and achieve their strategic objectives.
Entrepreneur
Entrepreneurs start and run their own businesses. If you're interested in starting a business that uses machine learning, this course will provide you with the foundational knowledge you need to get started.
CIO
CIOs are responsible for the overall IT strategy and infrastructure of an organization. As machine learning becomes increasingly important for businesses, CIOs need to understand the technology and its potential impact on their organization.

Reading list

We've selected 14 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 The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats.
Is written by the same instructor of this course and provides further depth in the business application of machine learning.
Covers a wide range of machine learning algorithms and techniques, providing a comprehensive resource for practitioners.
Covers the probabilistic foundations of machine learning, providing a deeper understanding of the underlying principles.
Covers the theory and practice of reinforcement learning, a type of machine learning that is used to train agents to make decisions in complex environments.
Covers introductory machine learning algorithms and techniques that are useful in business. It good reference for readers who want to deepen their understanding of what is covered in the course.
Covers data mining and analytics mindset, which is useful for understanding the process of deploying machine learning models.

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