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.
No Mandatory Login Times
4-6 Hours of Work per Module
“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.”
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.
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.
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.
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.
Demonstrate applications of static and moving image processing. Explain how data augmentation contributed to image processing. Illustrate applications of computer vision.
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).
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.
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.
Learn about data governance, database systems, API standards and more. Enroll in Machine Learning and Industry from EmergingEd.
Learn about natural language processing, neural networks, cluster analysis and more. Enroll in Machine Learning Foundations and Frameworks from EmergingEd.
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.