AI Fundamentals From Machine Learning to Agentic AI

This course cuts through the noise and builds your AI literacy; You’ll learn what an LLM is, how they think, why training data determines reliability, and how token-based pricing is reshaping enterprise economics.

You will learn to distinguish between grounding strategies like RAG, fine-tuning, MCP and hybrid approaches. You will learn the critical importance of training data quality for reliable, context-aware answers. Dive deep into the real-world risks of AI hallucination, bias, misalignment, and data confidentiality, and walk away with actionable governance frameworks to ensure responsible, high-impact AI adoption in your organization.

• How do traditional machine learning, generative AI, and agentic AI differ?

• What is an LLM, and why do the quality and scope of its training data directly determine the reliability of its answers?

• What do tools like Galileo, Copilot, and HR agents reveal about their real-world impact on work?

• What are tokens, context windows, and token-based pricing — and how do they explain the enterprise shift from license models to usage-based AI economics?

• What is the difference between grounding a model with RAG and changing its behavior through fine-tuning — and when should an organization choose one over the other?

• What are the key AI risks enterprises face today, and what governance practices can organizations put in place to manage them responsibly?