Hands-on! Building Intermediate AI Agent Services with LangChain and LangGraph: From RAG to Multi-Agents

Beyond a chatbot that simply generates answers, you will directly implement an autonomous AI Agent that makes its own judgments, calls tools, and performs multi-step tasks. You will learn through step-by-step practice, covering everything from OpenAI Agents SDK's Tool Calling, Handoff, Guardrail, Session, and Tracing to connecting external tools and databases using MCP, as well as LangChain and Deep Agents' Planning, Subagents, Filesystem, and Memory. The ultimate goal is to equip you with the ability to design and implement tool-integrated AI Agents that can be applied to real-world tasks.

Recently, all the remarkable achievements in the field of artificial intelligence are being announced in the area of reinforcement learning. This covers reinforcement learning technology—which is bringing about true innovation in AI such as robotics, autonomous driving, and humanoid machines—from basic to advanced levels in an easy-to-understand way for beginners.

Learn how to build deep learning models yourself using intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

Are you new to coding or looking to take your coding skills to the next level? This course will introduce you to Python, a powerful and flexible language, designed for all levels of learners. Taught in a “follow-along” fashion, this course will allow you to learn by actually writing and running code. You’ll discover why Python is one of the most popular programming languages. From artificial intelligence to web development, learning the Python language offers endless possibilities, and it’s an opportunity to greatly expand your personal potential.

Do you know the theory but feel lost when applying it to real-world data? I will pass on practical techniques to solve complex fraudulent transactions directly with code, incorporating my professional know-how.

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

Do you know the basics but feel stuck when it comes to practical application? I will clearly break down complex LLM structures by incorporating real-world industry experience.

You can acquire an overview of machine learning and deep learning, how to use basic tools, and the Python language knowledge necessary for learning in a short period of time.

Are you feeling overwhelmed by practical application and interview preparation? Based on my industry experience, I will help you firmly grasp everything from CNN to DETR, with a strong focus on code.

Using ChatGPT is completely different from developing an actual generative AI service. This is because you must master everything from API calls to conversation state management, structured outputs, function calling, document-based RAG, MCP integration, and AI agent implementation. Based on extensive experience in data analysis and AI instruction, this course explains complex concepts through Python-centered practice. You will learn step-by-step—from the principles of generative AI to the OpenAI Responses API, multimodal features, File Search-based RAG, external tool integration, and the Agents SDK—to complete a fully functional AI application.

If you only know LangChain, it's just a chatbot; but if you know LangGraph and Multi-Agent patterns, it becomes a service. An active AI instructor will share the know-how to build operational AI Agent workflows from start to finish—going beyond simple prompt calls to handling tools, maintaining memory, and incorporating human-in-the-loop verification—using 17 practice notebooks and 4 multi-agent patterns (Subagents, Handoffs, Router, and Skills).

Are you feeling stuck trying to implement an agent that goes beyond a simple chatbot? Based on my own trial and error and know-how from official documentation, I will show you how to build intermediate-level agents that can be immediately applied to real-world tasks.

Are you feeling stuck on how to apply what you've learned to real-world tasks even after mastering the basics? I will help you complete complex RAG and agent designs using my professional industry know-how.

This course covers everything from the basic concepts of recommendation systems to the principles of applying deep learning. Develop your practical skills for recommendation service development by learning various recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid recommendation systems!

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

Find your way through the sea of data and explore the future of artificial intelligence. This course explores the two massive technological trends of Big Data and Artificial Intelligence (AI), examining how they are bringing innovation to our lives and work.

Basic knowledge is not enough for real-world investing; build your own profit model using the machine learning know-how of a 10-year veteran quant.

Do you want to take your first step into the world of data? Learn machine learning and deep learning, the core technologies of AI, with Python. This course will guide you step by step, from the basics of machine learning and deep learning to practical applications. Traditional machine learning and deep learning are based on many of the same principles and technical systems. Therefore, this course does not separate the two into separate subjects, but rather organizes them into one connected course so that beginners can increase their understanding of machine learning as a whole.

JavaScript, which is known to all web developers, now explore the world of machine learning with this powerful language! This course learns how to build and deploy machine learning models using JavaScript and the powerful machine learning library Tensorflow.js. It guides you step-by-step through all the technologies required to develop web-based machine learning applications. Through this course, learners will systematically understand the core principles of machine learning. In addition, you will learn how to develop deep learning models using JavaScript and Tensorflow.js APIs, how to utilize transfer learning based on pre-trained models, and how to apply all this knowledge interactively in a browser environment.

The era of AI simply providing answers is over. Now is the age of LLM Agents that make decisions and take actions on their own. This course is an introductory lecture where you learn the core principles and structure of agents by implementing them yourself in just 90 minutes of hands-on practice. With minimal complex theory and a code-focused practical flow, you can directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

From PC control to high-level research and app development A guide to building your own 'AI Work Assistant' ready for immediate real-world deployment

Master Claude Code from the basics to a professional level. Learn core features (CLAUDE.md, sub-agents, MCP, Hooks, etc.) to develop and deploy actual services, concluding with basic appendices for non-majors and advanced application lectures.

Document (SDD) → Code Automation. Make the system work instead of typing prompts. Build your own AI development agent with Skills, Sub-agents, and Commands, and complete monetization services through 5 real-world projects.

Master the complete lifecycle of building modern Generative AI applications using Large Language Models, Retrieval-Augmented Generation, and Agentic AI systems. Learn to design enterprise-grade AI solutions from prompt engineering to deployment, combining LLMs with vector databases and external knowledge sources for production-ready applications.

It’s okay if you don’t know how to code. This course focuses on the experience of using AI to build an ad-revenue-generating Android app from scratch and successfully launching it in just one day. Many people stop at these stages: “I built the app, but how do I monetize it?” “I tried adding ads but got stuck on AdMob, policies, and reviews.” “I gave up right before launch because of the privacy policy.” In this course, we will navigate through every "drop-off point": 👉 Idea → App Creation → Ad Integration → Store Launch Essentials → Actual Deployment. We let the AI do the work, and we make the decisions. The goal is to complete an app with a "functioning revenue structure" within a single day.

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

How long will you keep struggling with repetitive daily research and document writing? I, too, was an ordinary office worker who worked overtime every day trying to find key insights amidst an overflow of information. However, after integrating Gemini 3 into my workflow, I experienced a significant increase in work speed. Boost your productivity by utilizing Gemini in your tasks.

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

This course trains your frontend system design mindset through real-world service cases such as Threads feeds, Amazon shopping carts, Netflix streaming UI, Google Docs collaborative editing, Micro Frontends, Agentic UI, and Observability.

Let's create a service that improves itself even while you sleep! Learn step-by-step how to build a 24-hour vibe coding system using loop engineering with Codex.

You use ChatGPT, but haven't you found it difficult to explain why it gives certain answers? "I know terms like RAG, Agents, and Fine-tuning... but it's hard to explain them accurately." "I get tongue-tied whenever I hear LLM-related terminology." "Conceptual explanations are always vague during AI meetings." This course was created specifically for people like you. This is a theoretical course designed to help you understand LLMs not just as a 'tool,' but as a 'structure.' It doesn't teach you how to use ChatGPT or Gemini; instead, it establishes the foundation so you can explain exactly why they work the way they do.

A practical introductory course on Claude Code for Unity developers! From installing Claude Code to Unity MCP integration, managing project conventions using CLAUDE.md, custom slash commands, and collaboration workflows—we will teach you everything you need to know for real-world application. Finally, you will gain hands-on experience effectively applying Claude Code to a Unity project by building a Survivor-style game from scratch.

Build a Kiwoom Securities API-based automated stock trading system from scratch using the core features of Claude Code (MCP, Agents, Skills). It's perfectly fine for beginners. Get started with Claude Code right now!

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

I will teach you Vibe Coding systematically from the ground up, following the exact curriculum refined while teaching over 10,000 students by a former Tech Lead with 10 years of experience in New York and LA startups. Together, we will build my own service, 'Kagong Map,' from scratch, learning the entire real-world workflow—from research, development, and deployment to operation and automation by delegating tasks to AI agents. I promise you two things: first, you will grasp the big picture of how a service operates so you can judge the AI's output yourself; second, you will learn how to embed workflows and standards into the environment so the AI produces consistent results every time. When you make the judgments and the AI follows orders effectively, your ideas become real services.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

This is a tutorial-style Claude code development course that even beginners and those new to development can easily follow. Based on experience teaching offline classes for planners, designers, and non-developers, you can learn everything in a single flow—from basic examples not covered in books to intermediate development projects. Using Claude code, you will experience the step-by-step development process, starting from "Hello World" to a Lucky Draw app, a device-optimized cafe menu management web page integrated with Claude design and Excel data, and finally a SaaS web application that can be uploaded to databases and cloud services. Tutorials are provided in stages of theory, practice, and projects. Through six levels of hands-on exercises, you will master AI-collaborative development methodologies and experience the entire development cycle, including cloud deployment and operation.