Full-Stack Generative AI and AI Agents with Python

As artificial intelligence evolves from simple chat interfaces to autonomous systems, the demand for developers who can build end-to-end AI applications is soaring. If you want to move beyond basic prompts and build intelligent agents that can reason, use tools, and access external data, you need a solid foundation in modern AI engineering. This text-based course guides you from the absolute basics of Python and AI terminology to designing, testing, and deploying production-ready generative AI systems. Through clear written explanations, structured code walkthroughs, and conceptual breakdowns, you will learn how to architect retrieval-augmented generation (RAG) pipelines and multi-agent workflows that solve complex, real-world problems. What you'll learn: - Understand foundational AI concepts, including tokenization, embeddings, large language models (LLMs), and how transformer architectures process information. - Practice modern Python development using type hints, Pydantic for structured data validation, and async/await syntax for high-performance applications. - Build retrieval-augmented generation (RAG) pipelines using vector databases to query and retrieve context from custom datasets. - Design autonomous AI agents and multi-agent systems using LangChain and LangGraph to manage complex, stateful workflows. - Configure secure, containerized environments using Docker to ensure seamless deployment and scalability of your AI services. - Apply advanced prompt engineering techniques and structured output schemas to guarantee reliable model responses. The course begins with essential terminology, Python fundamentals, and the core mechanics of language models. From there, you will progress through structured text lessons covering prompting, vector search, agent state management, and containerized deployment strategies. This course is designed for beginners and aspiring AI engineers who want a clear, text-focused pathway into the world of generative AI. No prior experience with artificial intelligence or machine learning is required. Start reading today to master the tools and architectures driving the next generation of intelligent software.