Full stack generative and Agentic AI with python

<div>Welcome to the Complete AI &amp; LLM Engineering Bootcamp – your one-stop course to learn Python, Git, Docker, Pydantic, LLMs, Agents, RAG, LangChain, LangGraph, and Multi-Modal AI from the ground up.</div><div><br></div><div>This is not just another theory course. By the end, you will be able to code, deploy, and scale real-world AI applications that use the same techniques powering ChatGPT, Gemini, and Claude.</div><div><br></div><div>What You’ll Learn</div><div><br></div><div>Foundations</div><div><ul><li><span style="font-size: 1rem;">Python programming from scratch — syntax, data types, OOP, and advanced features.</span></li><li><span style="font-size: 1rem;">Git &amp; GitHub essentials — branching, merging, collaboration, and professional workflows.</span></li><li><span style="font-size: 1rem;">Docker — containerization, images, volumes, and deploying applications like a pro.</span></li><li><span style="font-size: 1rem;">Pydantic — type-safe, structured data handling for modern Python apps.</span></li></ul></div><div><br></div><div>AI Fundamentals</div><div><ul><li><span style="font-size: 1rem;">What are LLMs and how GPT works under the hood.</span></li><li><span style="font-size: 1rem;">Tokenization, embeddings, attention, and transformers explained simply.</span></li><li><span style="font-size: 1rem;">Understanding multi-head attention, positional encodings, and the "Attention is All You Need" paper.</span></li></ul></div><div><br></div><div>Prompt Engineering</div><div><ul><li><span style="font-size: 1rem;">Master prompting strategies: zero-shot, one-shot, few-shot, chain-of-thought, persona-based prompts.</span></li><li><span style="font-size: 1rem;">Using Alpaca, ChatML, and LLaMA-2 formats.</span></li><li><span style="font-size: 1rem;">Designing prompts for structured outputs with Pydantic.</span></li></ul></div><div><br></div><div>Running &amp; Using LLMs</div><div><ul><li><span style="font-size: 1rem;">Setting up OpenAI &amp; Gemini APIs with Python.</span></li><li><span style="font-size: 1rem;">Running models locally with Ollama + Docker.</span></li><li><span style="font-size: 1rem;">Using Hugging Face models and INSTRUCT-tuned models.</span></li><li><span style="font-size: 1rem;">Connecting LLMs to FastAPI endpoints.</span></li></ul></div><div><br></div><div>Agents &amp; RAG Systems</div><div><ul><li><span style="font-size: 1rem;">Build your first AI Agent from scratch.</span></li><li><span style="font-size: 1rem;">CLI-based coding agents with Claude.</span></li><li><span style="font-size: 1rem;">The complete RAG pipeline — indexing, retrieval, and answering.</span></li><li><span style="font-size: 1rem;">LangChain: document loaders, splitters, retrievers, and vector stores.</span></li><li><span style="font-size: 1rem;">Advanced RAG with Redis/Valkey Queues for async processing.</span></li><li><span style="font-size: 1rem;">Scaling RAG with workers and FastAPI.</span></li></ul></div><div><br></div><div>LangGraph &amp; Memory</div><div><ul><li><span style="font-size: 1rem;">Introduction to LangGraph — state, nodes, edges, and graph-based AI.</span></li><li><span style="font-size: 1rem;">Adding checkpointing with MongoDB.</span></li><li><span style="font-size: 1rem;">Memory systems: short-term, long-term, episodic, semantic memory.</span></li><li><span style="font-size: 1rem;">Implementing memory layers with Mem0 and Vector DB.</span></li><li><span style="font-size: 1rem;">Graph memory with Neo4j and Cypher queries.</span></li></ul></div><div><br></div><div>Conversational &amp; Multi-Modal AI</div><div><ul><li><span style="font-size: 1rem;">Build voice-based conversational agents.</span></li><li><span style="font-size: 1rem;">Integrate speech-to-text (STT) and text-to-speech (TTS).</span></li><li><span style="font-size: 1rem;">Code your own AI voice assistant for coding (Cursor IDE clone).</span></li><li><span style="font-size: 1rem;">Multi-modal LLMs: process images and text together.</span></li></ul></div><div><br></div><div>Model Context Protocol (MCP)</div><div><ul><li><span style="font-size: 1rem;">What is MCP and why it matters for AI apps.</span></li><li><span style="font-size: 1rem;">MCP transports: STDIO and SSE.</span></li><li><span style="font-size: 1rem;">Coding an MCP server with Python.</span></li></ul></div><div><span style="font-size: 1rem;"><br></span></div><div><span style="font-size: 1rem;">Real-World Projects You’ll Build</span></div><div><ul><li><span style="font-size: 1rem;">Tokenizer from scratch.</span></li><li><span style="font-size: 1rem;">Local Ollama + FastAPI AI app.</span></li><li><span style="font-size: 1rem;">Python CLI-based coding assistant.</span></li><li><span style="font-size: 1rem;">Document RAG pipeline with LangChain &amp; Vector DB.</span></li><li><span style="font-size: 1rem;">Queue-based scalable RAG system with Redis &amp; FastAPI.</span></li><li><span style="font-size: 1rem;">AI conversational voice agent (STT + GPT + TTS).</span></li><li><span style="font-size: 1rem;">Graph memory agent with Neo4j.</span></li><li><span style="font-size: 1rem;">MCP-powered AI server.</span></li></ul></div><div><br></div><div>Who Is This Course For?</div><div><ul><li><span style="font-size: 1rem;">Beginners who want a complete start-to-finish course on Python + AI.</span></li><li><span style="font-size: 1rem;">Developers who want to build real-world AI apps using LLMs, RAG, and LangChain.</span></li><li><span style="font-size: 1rem;">Data Engineers/Backend Developers looking to integrate AI into existing stacks.</span></li><li><span style="font-size: 1rem;">Students &amp; Professionals aiming to upskill in modern AI engineering.</span></li></ul></div><div><br></div><div>Why Take This Course?</div><div><br></div><div>This course combines theory, coding, and deployment in one place. You’ll start from the basics of Python and Git, and by the end, you’ll be coding cutting-edge AI applications with LangChain, LangGraph, Ollama, Hugging Face, and more.</div><div><br></div><div>Unlike other courses, this one doesn’t stop at “calling APIs.” You will go deeper into system design, queues, scaling, memory, and graph-powered AI agents — everything you need to stand out as an AI Engineer.</div><div><br></div><div>By the end of this course, you won’t just understand AI—you’ll be able to build it.</div>

What you'll learn:

• Write Python programs from scratch, using Git for version control and Docker for deployment.
• Use Pydantic to handle structured data and validation in Python applications.
• Understand how Large Language Models (LLMs) work: tokenization, embeddings, attention, and transformers.
• Call and integrate APIs from OpenAI and Gemini with Python.
• Design effective prompts: zero-shot, one-shot, few-shot, chain-of-thought, persona-based, and structured prompting.
• Run and deploy models locally using Ollama, Hugging Face, and Docker.
• Implement Retrieval-Augmented Generation (RAG) pipelines with LangChain and vector databases.
• Use LangGraph to design stateful AI systems with nodes, edges, and checkpointing.
• Understand Model Context Protocol (MCP) and build MCP servers with Python.