About
Hands-on guide to modern AI: Tokenization, Agents, RAG, Vector DBs, and deploying scalable AI apps. Complete AI course What you 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. Requirements No prior AI knowledge is required — we start from the basics. A computer (Windows, macOS, or Linux) with internet access. Basic programming knowledge is helpful but not mandatory (the course covers Python from scratch). Descripition 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.