Machine Learning Cases

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Data Science Discipline:

Machine Learning Cases

This is your chance to learn from the successes and failures of others to build your machine learning skillset. Rooted in real cases, this course provides an opportunity for you to explore the details of applying machine learning in an organization with the guidance of an expert. You will learn how to evaluate machine learning applications in business and gain the confidence to utilize complex machine learning situations such as Turing Tests, robotic process automation, knowledge-based systems and recursive neural networks.

8 weeks
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Continuous Enrollment

Installment payments are now available on all course purchases!

Knowledge Areas

Turing Test
Robotic Automation
Knowledge-based Systems
Fraud detection
Recursive neural network
Event logs
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8 Modules

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4-6 Work Hours Per Module

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Thomas Miller, Ph.D.

Your Subject Matter Expert:

Thomas Miller, Ph.D.

“It is estimated that over half of the traffic on the web is robots. These are programs that are doing things, programs that various companies are using to gather information to interact with websites, to act like they’re users of websites collecting information.”

Course Modules

This machine learning course is perfect for professionals with experience in machine learning or those who have an interest in examining the lessons learned from existing applications of machine learning. Immerse yourself in practical learning scenarios developed from real-world cases and utilize the insights you'll gain to select the right system architectures and apply machine learning appropriately based on business requirements.

Module 1: Economics and Finance

Review the results of forecasting models. Examine findings from competitive intelligence. Describe methods of financial engineering as well as methods for anomaly and fraud detection.

Module 2: Operations and Logistics

Show how event logs are important to process management. Illustrate the applications of machine learning in manufacturing as well as supply chains and distribution. Describe the uses of machine learning in quality management.

Module 3: Product Design and Pricing

Show how machine learning may be employed in understanding consumers and markets. Illustrate methods for strategic product positioning and design. Explain how machine learning research may inform pricing policy. Employ machine learning methods in acquiring and retaining customers.

Module 4: Search and Recommendation

Draw inferences about relationships from graph databases. Demonstrate information retrieval from document collections/search applications. Describe methods for making product recommendations and illustrate applications of social network analysis.

Module 5: Image Processing

Demonstrate applications of static and moving image processing. Explain how data augmentation contributed to image processing. Illustrate applications of computer vision.

Module 6: Natural Language Processing

Compare natural language processing tasks in terms of difficulty. Describe applications of machine learning to document classification. Explain neural network approaches to machine translation. Illustrate applications of conversational agents (chatbots).

Module 7: Knowledge-Based Systems

Define knowledge engineering as employed in first- and second-generation expert systems. Describe varieties of knowledge bases. Employ an open-domain knowledge base to answer questions about the world. Develop a plan for building a domain-specific knowledge base.

Module 8: Intelligent Agents and Robotics

Discuss the Reinforcement Learning Foundations of Robotics. Contrast various forms of intelligent agents and robotics. Describe applications of robotic process automation. Imagine how intelligent personal assistants can improve the quality of life.

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Thomas Miller, Ph.D.

Thomas W. Miller is faculty director of the data science program at Northwestern University. He designed distance learning training materials for the program, including courses in advanced modeling techniques, marketing analytics, data engineering and machine learning. Dr. Miller has published six books about data science with Pearson Education (the series "Modeling Techniques in Predictive Analytics"). He also owns a publishing and consulting firm, Research Publishers LLC, located in Manhattan Beach, California. He provides data science consulting services and is actively involved in developing chatbot and survey research applications. Earlier in his career, Dr. Miller worked as a network engineer for NCR Comten and as a field engineer and account representative for Hewlett-Packard Company. He also directed the A.C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison.

Dr. Miller holds a doctorate in psychology and a master's degree in statistics from the University of Minnesota, as well as an MBA and a master's degree in economics from the University of Oregon.