Learning TypeScript with AI — From Basic Syntax to Full-Stack App Deployment

Are you simply telling Codex, “Build it for me”? It works well at first. But as the project grows, the AI modifies the wrong files, forgets the rules, and repeats the same mistakes. In this course, you’ll learn how to use the **latest Codex** to delegate coding while keeping control over **design, permissions, context, and verification** as a developer. **Design → Plan → Implement → Test → GitHub PR** Based on the developer’s judgment and review, AI development automation is applied throughout this entire process.

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.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

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.

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

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!

Are you just telling Claude Code, “Build it for me”? It works well at first. But as the project grows, the AI modifies the wrong files, forgets the rules, and repeats the same mistakes. In this course, you’ll learn how to leverage **the latest Claude Code** to delegate coding while still keeping the developer in control of **design, permissions, context, and verification**. **Design → Plan → Implement → Test → GitHub PR** Based on the developer’s judgment and review, AI-powered development automation is applied throughout this entire process.

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.

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!

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.

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.

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.

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.

Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

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.

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