RAG Master: From Basics to Advanced Techniques (feat. LangChain)

This is a basic Python course for non-major beginners. Theory textbooks are provided through Wikidocs, and practice materials are provided through Github.

Neo4J-based Knowledge Graph RAG System: Next-Generation Search Technology That Goes Beyond Simple Vector Search to Leverage Data Relationships Maximizing RAG Performance with the Power of Graph Databases!

Implementing a Retrieval-Augmented Generation (RAG) Intelligent AI Agent with LangGraph! From theory to practice, this is a hands-on tutorial that even beginners can easily follow.

Learn LangChain's basic concepts and usage with Python. Hands-on practice will be conducted primarily in a Google Colab (colab) environment, with materials provided via Github.

This course is designed for beginners and consists of easy explanations and various hands-on projects. It covers data collection using web crawling and LangChain tools, and summarization, extraction, sentiment analysis, and recommendation systems using LLM.

Create your own AI chatbot using Python's basic syntax and libraries. Learn through a step-by-step process of completing 5 projects, including PDF document-based RAG, and deploy them as web services.

No development knowledge needed! Create your own automation system with just a few clicks.

What if ChatGPT were embedded within your own application? In this lecture, we will develop various programs using the ChatGPT API. We will cover all processes in detail, from basic API usage to program creation and deployment.

LLM: A pro quickly delivers core content, from essentials to practical tips!

Chatgpt is a program that reads and comments on posts written by my neighbors Lesson on developing marketing automation and neighbor management programs

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

Do you still code directly these days? Now is the era where even vibe coding is too much hassle! Become an AI automation expert with n8n without coding. A practical automation workflow design guide you can apply immediately at work Action-oriented automation strategies that maximize work efficiency. Build intuitive workflows with n8n and experience workplace innovation

You can learn everything from basic usage of generative AI to automation and creating customized AI chatbots for practical work applications all in one comprehensive course.

During the Gold Rush, it was said that it was easier to become rich by selling gold mining tools than by mining gold yourself. How about selling programs in the ChatGPT Age of Exploration?

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

The goal is to master the complex GPT API and Python in the easiest way possible through hands-on practice. You will develop a YouTube video summarization AI using the latest ChatGPT API and implement it as a web application using Streamlit.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

Automate Creating YouTube Shorts with AI!! Learn from scratch how to automatically create YouTube Shorts with n8n. Build a complete workflow that runs from text→image/music→video creation→upload all at once. Practice distinguishing between test mode/production mode and tips for saving costs. Complete 3 ready-to-use automation templates by the end of the course. Learn step-by-step from node placement so you can follow along even without coding knowledge. Let's automate AI YouTube Shorts production together!

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

No coding knowledge required. Build it instantly in your browser with Google's latest tool (IDX)—no installation needed! Stop studying coding syntax! This is a high-speed practical course where you'll build an RAG chatbot that perfectly understands internal documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.

Create a program using the GPT API and even generate images and videos!

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice. Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop. Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios. Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services. 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph 👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo" 👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).