LearnJavaScript Beggom · 09-Окт-25 17:00(18 дней назад, ред. 09-Окт-25 17:01)
Full stack generative and Agentic AI with python Год выпуска: 8/2025 Производитель: Udemy Сайт производителя: https://www.udemy.com/course/full-stack-ai-with-python/ Автор: Hitesh Choudhary, Piyush Garg Продолжительность: 32h 10m 0s Тип раздаваемого материала: Видеоурок Язык: Английский Субтитры: Английский Описание: Hands-on guide to modern AI: Tokenization, Agents, RAG, Vector DBs, and deploying scalable AI apps. Complete AI course 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.
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.
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).
Description 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.
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.
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. Формат видео: MP4 Видео: avc, 1280x720, 16:9, 30.000 к/с, 2298 кб/с Аудио: aac lc, 48.0 кгц, 128 кб/с, 2 аудио
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