Foundations of AI – ML, GenAI And Agentic AI

What Will You Learn?

• Grasp the core concepts of Machine Learning, including the differences between traditional programming and ML approaches
• Understand key ML types – supervised, unsupervised, and reinforcement learning – in simple, non-technical terms
• Explore the basics of common ML algorithms like linear/logistic regression, decision trees, neural networks, and boosting methods
• Learn how models are built, evaluated, and improved through feature engineering, training/inference cycles, and tuning
• Get introduced to the world of Large Language Models (LLMs) – understand how they work, what they can do, and their limitations
• Discover the fundamentals of Agentic AI – including memory, planning, agent architectures, tools, and real-world use cases
• Reflect on the ethical considerations of Agentic AI and how it ties into modern practices like DevOps and MLOps