This module establishes the groundwork for all subsequent AI learning. You'll explore the history and evolution of AI, understand key terminology, and differentiate between various AI approaches. Learn about supervised, unsupervised, and reinforcement learning paradigms, and examine real-world applications across industries.

Dive deep into the architecture and applications of generative AI models. Learn how transformer-based models like GPT-4 generate human-like text, how diffusion models create stunning images, and how code generation models assist developers. Master prompt engineering techniques to maximize model performance and explore real-world business applications.

Move beyond generative AI to explore autonomous AI agents. Learn how agents perceive their environment, reason about goals, plan sequences of actions, and execute tasks. Understand the fundamental differences between passive generative models and active agentic systems, and explore the architectures that enable autonomous decision-making.

Explore the architectural patterns that power effective AI agents. Learn about different agent design paradigms, memory systems that enable context retention, tool integration for external interactions, and decision loops that guide agent behavior. Study real-world examples of agent architectures and learn to design your own from scratch.

This module introduces the fundamentals of single-agent AI systems. Learners explore how autonomous agents perceive environments, make decisions, and execute actions toward specific goals. Emphasis is placed on practical design patterns, task decomposition, and integrating tools such as APIs and databases for real-world applications.

This module explores systems where multiple AI agents interact, collaborate, and negotiate to achieve shared or competing goals. Learners will understand coordination strategies, communication protocols, and conflict resolution techniques used in advanced AI applications such as simulations, robotics, and enterprise automation.

This applied module compares generative AI systems with agentic AI architectures. Learners gain hands-on insight into how generative models create content, while agentic systems plan, reason, and act autonomously. Practical scenarios demonstrate how combining both approaches delivers powerful AI solutions.

This module focuses on evaluating AI systems using quantitative and qualitative metrics. Learners explore model accuracy, robustness, bias detection, and human-in-the-loop evaluation techniques. Emphasis is placed on aligning technical metrics with business and user outcomes.

This module addresses the ethical, legal, and societal implications of AI. Learners explore responsible AI principles, risk mitigation strategies, data governance, and regulatory considerations. The module equips participants to design AI systems that are transparent, fair, and aligned with organizational values.

Apply everything you've learned to a comprehensive capstone project. Work with industry mentors to identify a real-world problem, design an appropriate AI solution, implement the system, and evaluate its performance. Present your project to a panel of experts and receive actionable feedback. This project becomes a centerpiece of your professional portfolio.