Next week November 2-4 is the first of our live fall trainings – Operationalizing Machine Learning with DMN. We recommend signing up for it in combination with the second training that runs from November 9-11 the following week. By registering for both classes at the same time you will receive a 15% discount on the classes.
In the first course, you will get a deep understanding of the concepts and methods involved in operationalizing machine learning to deliver business outcomes. At the end of the course you can be immediately effective.
Here are some of the things you will learn in the first course:
• Apply machine learning to business operations through the structure of CRISP-DM
• Use decision modeling to understand real-world business problems in a way that allows machine learning to be applied effectively
• Take a decision-centric and business-focused approach to machine learning projects
• Evaluate and deploy machine learning results to minimize the gap between analytic insight and business improvement
The second course on Decision Modeling will give you a deep understanding of the Decision Modeling and Notation (DMN) standard. You’ll learn a step-by-step process on how to identify and prioritize the decisions that drive your success, see how to analyze and model these decisions, and understand the role these decisions play in delivering more powerful information systems.
In the second course learn how you can use decision modeling to:
• Re-use, evolve, and manage business rules.
• Effectively frame the requirements for analytic projects.
• Streamline reporting requests.
• Define analytically driven performance dashboards.
• Optimize and simplify business processes.
By the end of the course you should be able to describe decision requirements using DMN standards, describe the core elements of a decision model, differentiate the types of decisions an organization makes and the suitability of decision modeling to the type of decision, and more. You’ll be able to demonstrate examples of how to elicit decision requirements, examine business rules and how business logic can be used in practice, and evaluate how analytics are embedded in the decision modeling process.