LangGraph in Action: Develop Advanced AI Agents with LLMs

In this course, you will learn how to design, build, and evaluate advanced RAG systems using the LangChain framework. You will master LCEL, Runnables, advanced retrieval techniques, chunking strategies, cross-encoder reranking, agent-based RAG, tool calling, SQL integration, and safety techniques using NeMo Guardrails. You will also learn how to trace, debug, and deploy a full-stack AI chatbot with LangFuse, React, FastAPI, and Docker.

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 directly through just 90 minutes of hands-on practice. We minimize complex theory and focus on code-centered practical flow, allowing you to directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.

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

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

Python Korean Text Analysis and Natural Language Processing: Word Cloud Visualization, Morphological Analysis, Topic Modeling, Clustering, Similarity Analysis, Bag of Words and TF-IDF for Text Data Vectorization, Text Classification Using Machine Learning and Deep Learning, and How to Use Hugging Face

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.

If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. Let's get started!

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!

All the recent amazing achievements in the field of artificial intelligence have been announced in the field of reinforcement learning. We have covered reinforcement learning technology, which is revolutionizing true artificial intelligence technology such as robots, autonomous driving technology, and human-like machines, from the basics to the advanced level, in an easy-to-understand way for beginners.

The goal is to learn complex GPT API and Python in the easiest and most hands-on way possible. Using the latest ChatGPT API, we'll develop a YouTube video summarization AI and implement it as a web application using Streamlit.

Automate YouTube Shorts Creation with AI!! Learn from scratch how to automatically create YouTube Shorts using n8n. Build a complete workflow that goes from text→images/music→video creation→upload all in one flow. Practice with test mode/production mode separation and cost-saving tips. Complete 3 ready-to-use automation templates by the end of the course. Learn step-by-step from node placement, even if you don't know coding. Let's automate AI YouTube Shorts production together!

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

This course focuses on M365 Copilot, a smart work partner in the Microsoft 365 ecosystem, and covers how to achieve work innovation by utilizing AI in an environment where security and copyright are protected. It introduces the main features of Copilot and various ways to leverage it in conjunction with Microsoft 365 services, while also presenting prompting techniques to elicit accurate and effective responses.

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.

We create an AI judge by combining CrewAI, facilitating LLM-based multi-agent technology, with knowledge graphs and inference systems.

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.