Practical Generative AI Mastery: LLMs, Intelligent Agents & Model Context Protocol for Beginners

What you will learn:

Master the core concepts of Generative Artificial Intelligence and distinguish its paradigm shifts from conventional AI.
Comprehend the high-level operational mechanics of Large Language Models (LLMs) without delving into complex mathematics.
Acquire advanced Prompt Engineering methodologies to optimize AI interactions and generate superior, consistent outputs.
Understand the sophisticated ways AI systems leverage context, internal memory, and external tools for enhanced performance.
Develop and deploy autonomous AI agents using powerful platforms like AWS Bedrock Agent.
Construct your own Model Context Protocol (MCP) server for bespoke AI integrations.
Grasp the intricacies of the Model Context Protocol (MCP) as the universal connector for AI models with diverse tools, data sources, and enterprise services.
Utilize pre-existing third-party MCP servers to establish seamless connections with external data ecosystems.
Gain hands-on experience running and experimenting with open-source AI models directly on your personal computing environment.

Description

The landscape of technology is being fundamentally reshaped by Artificial Intelligence. Specifically, advancements in Generative AI and powerful Large Language Models (LLMs) are democratizing access to intelligent tools, making them indispensable across every profession—from content creation and strategic analysis to process automation and developing sophisticated autonomous agents.

This meticulously crafted course serves as your definitive beginner-friendly gateway to cutting-edge AI. It offers a deeply practical, hands-on journey designed to demystify complex AI concepts and equip you with the tangible skills to confidently understand, utilize, and even build with today's most transformative AI systems. No prior background in AI or machine learning is required; we start from the absolute basics.

You will embark on a structured learning path, commencing with the foundational principles of Generative AI. Progress through understanding the inner workings of LLMs and master the crucial skill of prompt engineering to elicit precise and effective responses. The curriculum then delves into advanced, real-world applications such as Retrieval-Augmented Generation (RAG) for context-aware AI, the architecture and implementation of intelligent AI agents, and the burgeoning standard of the Model Context Protocol (MCP)—essential for enabling AI models to interact seamlessly with external tools and systems.

Upon successful completion, you will transcend theoretical knowledge; you will possess the practical expertise to actively engage with, innovate, and deploy AI solutions.

Transformative Outcomes You Can Expect:

Articulate a clear understanding of modern AI paradigms, including Generative AI and LLM-driven architectures.
Expertly craft, refine, and optimize prompts to achieve superior outcomes from diverse AI tools and models.
Grasp the intricate design and operational mechanics behind advanced AI agents and MCP-enabled systems.
Cultivate the critical thinking necessary to make astute decisions regarding the integration, application, and development of AI-powered products.
Establish an exceptionally robust foundation, paving your way to specialize further in advanced AI research, agent development, or innovative AI application engineering.

Whether your ambition is to master new technologies, build groundbreaking applications, educate others on AI, or spearhead strategic AI initiatives within your organization, this course provides the comprehensive conceptual framework and practical toolkit you need to succeed in the AI era.

Curriculum

Getting first hand taste of Generative Ai on your local Machine

Dive directly into the world of AI with practical application. Learn to set up local AI environments using tools like Ollama on Mac and Windows, enabling you to run open-source models directly on your machine. Build your very first AI application, such as an English Tutor, for immediate hands-on experience. Explore powerful platforms like Google's NotebookLM for collaborative AI work and gain insights into the diverse landscape of widely used closed and open-source models, setting the stage for a comprehensive AI journey. This section also outlines the course roadmap and introduces your instructor.

Fundamentals of Generative AI & LLMs

Establish a solid theoretical foundation by understanding the core differences between Generative AI and traditional AI paradigms. Delve into the architecture and operational principles of Large Language Models (LLMs), learning how they process information and generate responses. Explore the concept of "tokens" as the fundamental units of text for LLMs, and master essential AI terminology including inference, context windows, the phenomenon of hallucinations, and an introduction to Retrieval-Augmented Generation (RAG).

Art of Prompt Engineering: Understanding And Crafting good Prompts

Unlock the power of effective communication with AI by mastering the art of prompt engineering. Understand the critical need for well-crafted prompts and explore key parameters like Temperature, Top-K, and Top-P that influence AI output creativity and focus. Deconstruct the components of a robust prompt and learn various advanced prompting techniques including Zero-Shot, One-Shot/Few-Shot, Chain-of-Thought (CoT), and ReAct (Reason and Act) prompting. Additionally, gain proficiency in leveraging system, contextual, and role-based prompting for highly targeted and effective AI interactions.

Agentic AI Fundamentals and Development

Venture into the exciting domain of Agentic AI, starting with a clear definition of what constitutes an AI agent and exploring its essential components. Get hands-on with serverless agent development using AWS Bedrock Agent, understanding its capabilities and architecture. Delve into the specifics of an AIOPS AI agent's architecture and prerequisites, followed by practical steps to set up AWS CLI and credentials. Conclude by creating a functional AIOPS AI agent within AWS Bedrock, all while integrating and understanding the crucial principles of Responsible AI development.

Fundamentals of Model Context Protocol (MCP) and Use

Discover the Model Context Protocol (MCP), a transformative standard for secure and efficient AI model interaction with external systems and tools. Gain practical experience by deploying and experimenting with a Docker MCP server to understand its operational dynamics. Explore the basic architecture of MCP, then build your own MCP server from scratch, demonstrating its power by integrating it with a real-world application like Google Calendar for automated event management.

Conclusion

Consolidate your learning and reinforce key takeaways from the entire course. This concluding section provides a comprehensive wrap-up, ensuring you're fully equipped with the knowledge and confidence to apply your newly acquired AI skills in various professional contexts.

Deal Source: real.discount