graphRAG - Knowledge Graph-based RAG System Implemented with Neo4J (feat. LangChain)

This lecture covers RAG (Retrieval-Augmented Generation) system fundamentals and implementation using LangChain. Participants will understand RAG's core principles and learn to build and evaluate systems using LangChain.

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.

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

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.

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.

This lecture covers RAG (Retrieval-Augmented Generation) system fundamentals and implementation using LangChain. Participants will understand RAG's core principles and learn to build and evaluate systems using LangChain.

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.

Easy and fun SQL stories learned from professional instructors! Lectures are conducted systematically and step-by-step for beginners. We offer opportunities to learn with sufficient repetition through a 3-step learning process.

# Silicon Valley Internship That Won't Get Cut Even in the AI Era & Complete Course on 30 Big Tech System Designs & Open Source Practical Contributions All students who submit course missions can apply for an AI startup internship in Palo Alto, USA, with exemption from document screening and guaranteed interview opportunities. This course involves designing and scaling 30 real-world system designs based on big tech cases including Netflix, Tesla, Meta, and Tinder. Finally, you'll design your own service from architecture → DB → scaling, contribute to an actively operating open source AI coding agent project (achieving 1.8k downloads per week) to complete your portfolio, and finalize your resume.

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

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

This course is designed to provide step-by-step learning from the basic concepts of database systems to the utilization of Relational Database Management Systems (RDBMS) and SQL. Learners will understand the principles of databases and be able to create systematic data structures through data modeling and design methods. Additionally, they will experience actual data processing by practicing data manipulation and management techniques using SQL. Through this course, students can go beyond simple theoretical learning to build a foundation that can be utilized for both practical work and certification exam preparation by acquiring efficient data management and application skills.

A beginner's guide to SQL coding! We'll teach you the secrets of writing, interpreting, and validating SQL.

Based on Oracle 21C, from concepts to practice, the easiest way to learn SQL for beginners! Taught directly by the author of Easyspub's "Do it! Introduction to SQL with Oracle"! Designed for non-majors and beginners, explained step by step.

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

What do you learn in the 'Business Information Visualization Certification' course? This certification course focuses on developing the ability to analyze data well, visualize it in an easy-to-understand way, and clearly and effectively communicate the information needed when making important decisions in a company or organization. Simply put, it's a course where you learn the skills to not just look at data, but to discover the hidden meanings within it and express them through pictures, tables, graphs, etc., so you can explain them well to others. Through this course, you will: Develop the ability to think strategically through data, build practical skills that can be immediately applied to actual work, and enhance your professional expertise and competitiveness. Through this lecture, develop the ability to connect business and data, and take one more step toward becoming an expert who understands and explains data.

If you work with data, SQL is a fundamental skill you must know. In this course, we will learn SQL from a data analysis perspective, focusing on hands-on practice. The practice will be conducted in Google Colab using DuckDB.

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.

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

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

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!

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.