AI Agent with LangGraph version 1: From Basics to Advanced

This course is a practice-oriented lecture that goes beyond 👉 just explaining the concepts of RAG (Retrieval-Augmented Generation), 👉 to building the actual working architecture yourself, 👉 and experiencing expansion and optimization. Starting from simple RAG examples, you will learn step-by-step—from Advanced RAG to Modular RAG and Agent-based RAG—at a level that can be immediately applied in the field.

This course is a practice-oriented program that goes beyond simple chatbots to build multi-agent systems where multiple agents collaborate. 👉 You will learn three frameworks—Swarm, LangGraph, and DeepAgents—step-by-step, completing six practical projects including a customer support chatbot, blog content creation, a PostgreSQL query agent, and an autonomous research agent. 👉 By implementing enterprise-grade features such as long-term memory using Redis and PostgreSQL, Human-in-the-loop security mechanisms, and cost monitoring, you will be able to build production-level systems that can be applied to real-world services immediately after the course.

🧩 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.

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.

"Throw away the cliché AI theories and chatbot examples scattered all over YouTube. This is Korea's one and only '7 Major Industry Integrated Manufacturing AI Bible' that understands the language of manufacturing and pierces through the essence of the process." Hello, I am Byte Detective, your knowledge sharer. Currently, countless companies and practitioners are calling for 'Manufacturing DX (Digital Transformation)' and 'AI Adoption.' However, the majority of courses on the market are limited to vague theories from IT developers or consultants who have no understanding of the harsh practical realities of the manufacturing field. This course is not a lecture for the sake of theory. By organically combining the actual domain knowledge of Korea's top 7 representative manufacturing industries with the latest AI frameworks, it provides a 'Practical Manufacturing DX Scenario Architecture' that can be applied to the field starting tomorrow.

This course is a practice-oriented lecture that goes beyond 👉 just explaining the concepts of RAG (Retrieval-Augmented Generation), 👉 to building the actual working architecture yourself, 👉 and experiencing expansion and optimization. Starting from simple RAG examples, you will learn step-by-step—from Advanced RAG to Modular RAG and Agent-based RAG—at a level that can be immediately applied in the field.

Many people use ChatGPT and frequently hear news about recommendation systems or autonomous driving, but they find themselves at a loss for words when trying to explain "why it works this way." If you only follow coding lectures, the code runs but the principles remain a mystery; if you only take math lectures, you see the formulas but can't connect them to AI. Overseas lectures are burdened by the language barrier, while domestic introductory courses often lean too heavily on practice alone. I have seen the same scene repeated over and over while building actual models in AI competitions and meeting thousands of learners while running "Everyone's AI." The point where people get stuck isn't how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation feel disconnected from one another. This course connects those dots. From the basics of mathematics to neural networks and softmax, it explains in a single flow how functions define inputs and outputs, how derivatives determine the direction of learning, and how backpropagation transmits errors. This is a class designed to make you understand the "why" through formulas and intuitive illustrations, rather than just memorizing equations or copying code. For those who want to truly understand AI, this will be a solid first step toward your next level in deep learning.

Opening a new window, copying, and pasting into a messenger every single time — let today be the last! OpenClaw Practical Course: Your own AI assistant that works directly for you.

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

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!

Mastering difficult AI with just one single analogy?! A first step into AI principles, understanding through core concepts rather than just terminology.

We're building an AI-powered video conferencing application with real-time AI agents, meeting summary features, and post-call functionality.

The Master Guide to AI for Java Developers! This course shatters the prejudice that AI is the exclusive domain of Python and empowers your Java applications with AI-driven eyes, ears, voice, and even coding capabilities. Grow into an AI Architect based on the OpenAI API. You will directly develop various AI experts such as an AI writer, translator, artist, and coding companion. You will also build a multi-functional AI assistant that integrates core features including text summarization, sentiment analysis, image description, Java code generation, speech-to-text/text-to-speech conversion, and image/subtitle generation. This is a golden opportunity to maximize the competitiveness of existing Java systems by learning how to assign AI roles through system messages and even implement harmful content moderation.

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will gain a broad range of knowledge, from establishing AI-based system testing strategies to designing and executing test cases, as well as utilizing AI for test support. This course is based on the ISTQB AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

Understand the operating principles of generative AI models using deep learning and acquire practical application skills through hands-on exercises.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

[Let's Build!] Have you ever given up because you were tired of connecting nodes? "Create a daily morning briefing on Slack" — that's all it takes. Claude Code designs it, and the n8n server runs it 24/7 for you. [Let's Operate!] If adopting n8n was easy but managing it felt overwhelming, this lecture is the answer. "Organize everything according to the conventions" — dozens of workflows are tidied up at once. Creation, organization, monitoring, and backup—operate everything using natural language.

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

This course covers implementing combat, UI, data, and server functions for a Unity idle game using CLI and MCP-based AI tools. You will learn the entire process of releasing a mobile game to Google Play, including the application of Firebase, advertisements, in-app purchases, and Addressables.

Your colleagues are already using Claude Code for their work—are you still doing everything manually? Don't worry! This course will turn you into a vibe coding master.

AI agents seemed vague, didn't they? We pass on problem-solving know-how from practical experience, covering everything from prompts to loops and graph engineering systems.

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

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