Artificial Intelligence has moved from the realm of research into the everyday reality of business and IT. Most professionals now use AI-powered tools in their work, but many still misunderstand what an AI system actually is. This course defines what we mean by an AI system and shows how these systems fit into enterprise architectures.

Agentic AI systems are autonomous, goal-driven assemblies of models and tools that can plan, act, and interact with external systems with limited supervision. This course explains what agentic AI is, how Model Context Protocol enable agent orchestration, and why they introduce new attack surfaces.

Before any algorithm is trained or deployed, its success depends on one thing: the shape and structure of the data feeding it. This course establishes the foundation for understanding why data modeling (how information is organized, related, and represented) is the most critical stage of any AI or machine learning (ML) project.

Artificial Intelligence is not a single system or tool. It's a collection of different methods that learn, reason, and solve problems in unique ways. Each type of AI has its own strengths, limitations, and data requirements. Learn how these types differ, when to use each, and what tradeoffs are most important in production systems.

Every successful AI initiative, regardless of size or complexity, follows a repeatable process known as the machine learning (ML) lifecycle. Learn the phases of a typical ML lifecycle and why adopting one is the single most important factor in delivering reliable, ethical, and cost-effective AI solutions.

Artificial Intelligence is reshaping how organizations make decisions, build products, and serve customers. Yet for every success story, there are many AI projects that fail compliance reviews, stall before launch, or create new risks because teams lacked clear, actionable best practices. This course was created to bridge that gap.