Full stack generative and Agentic AI with python

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