Machine Learning for Professionals

About this course

This course provides a rigorous, non-technical understanding of machine learning for professionals who work with ML-driven systems, decisions, or claims in real organizational environments. Participants learn how machine learning models are structured, how different classes of algorithms behave, and how these systems are applied across business, operational, legal, and compliance contexts.

The course begins with core machine learning concepts and algorithms, then connects them directly to real-world application patterns, data pipelines, and deployment realities. Participants examine how ML systems are trained, evaluated, monitored, and maintained over time, and why many systems fail after initial rollout despite strong demos or reported accuracy.

Throughout the course, machine learning is treated as a socio-technical system rather than a purely technical artifact. Topics such as evaluation metrics, drift, explainability, bias, safety, compliance, and governance are introduced in direct relation to how ML systems influence decisions, incentives, and accountability. The focus is not on building models, but on understanding their behavior, limits, risks, and economic impact.

This course is designed for professionals who want a clear, structured understanding of how machine learning works in practice, how it is applied across real systems and workflows, and what its capabilities, limitations, risks, and costs imply for modern organizations, without writing code or using mathematics.