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Hands-on projects integrating MCP with AI tools like Gmail, Twitter, and Zapier
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Deep exploration of building context-aware, scalable AI systems for real-world applications
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Step-by-step guidance on securing, containerizing, and deploying AI systems using Docker
This course introduces the Model Context Protocol (MCP) as a transformative framework for AI automation. You’ll start by understanding the core concepts of MCP and move on to practical projects, including integrating AI tools with Gmail, Twitter, and Zapier. The course focuses on building scalable, context-aware AI systems that reduce errors like hallucinations and improve performance.
As you progress, you’ll learn how to structure and layer context for optimal AI interaction. With real-world use cases and hands-on projects, you’ll explore how to build AI systems from scratch, design reusable context blocks, and mitigate drift and context loss. The course is designed for professionals who want to shift from prompt engineering to system-level AI design.
In the final sections, you will deepen your understanding of MCP by developing and securing Docker containers for AI tools, ensuring your systems are reliable and scalable. By the end of this course, you'll be equipped to implement MCP in your own projects and systems, making you a proficient practitioner of modern AI system design.
This course is tailored for developers, prompt engineers, data scientists, and AI enthusiasts who want to move beyond basic prompt engineering. Ideal for professionals looking to optimize AI systems, integrate automation tools, or create advanced AI-driven applications. The course is also beneficial for product managers and designers working on AI-integrated tools or chatbot creators aiming to improve AI continuity and memory. No prior coding knowledge is required, but a tech-savvy mindset and curiosity about AI architecture are essential.
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Understand the fundamentals of the Model Context Protocol and its significance
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Learn to structure and layer context for efficient AI system interaction
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Explore how to prevent context drift, hallucinations, and errors in AI outputs
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Build modular and reusable context blocks for scalable AI systems
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Develop AI applications using MCP with tools like Gmail, Twitter, and Zapier
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Transition from prompt engineering to system-level AI architecture design
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