Large Language Models LLM for Everyone Part 5 - Building Your Own AI Agent with LangGraph

Non-Majors Welcome: Real-World Vibe Coding Projects Created Through Conversations with AI

From the basics of deep learning natural language processing to the latest models such as Transformer and BERT, learn the principles and utilization methods of deep learning natural language processing (NLP) through various examples and practical code implementations.

This course will teach you the core theories of deep learning and how to implement deep learning code using the latest TensorFlow 2.0.

This is a lecture where you build your own MCP server using Cursor and experience vibe coding, which embodies the future of developers.

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

This lecture covers building practical RAG (Retrieval-Augmented Generation) systems while developing an AI customer service chatbot (AICC).

This is a lecture where you can learn how to use Git & GitHub step by step from the basics to advanced content through various practical exercises. Understand the principles through intuitive examples and learn how to use Git & GitHub through practice.

Through AI company reviews, we examine how AI technology will change our present and future through various cases. By examining the necessary elements for carrying out AI projects, we prepare for the upcoming AI Transformation.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

Learn how to utilize various local LLMs (Qwen, Gemma) and explore different techniques to efficiently evaluate and improve the performance of LLM systems.

We will learn step by step from the basic concepts of LLM (Large Language Model) to how to fine-tune the Llama 2 model, a high-performance LLM, on the dataset of your choice.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

This is a lecture on creating an automatic stock trading bot using the IBK Investment & Securities Xing API. Create your own automatic stock trading bot that trades automatically 365 days a year.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

A course to learn website crawling methods using ChatGPT. Get a preview of the transformed future ChatGPT will create.

This course covers data analysis using ChatGPT and Pandas utilization. Get a preview of the changed future ChatGPT will create.

This lecture teaches various business automation methods using ChatGPT (Excel, PPT, crawling, image processing) and Python basics for business automation. Experience in advance the changed future that ChatGPT will create.

A course to learn deep learning paper implementation skills, implementing the GPT-1 (Improving Language Understanding by Generative Pre-Training) paper from scratch with TensorFlow 2.0.

This lecture teaches how to create diverse quant investment strategies using ChatGPT. Use ChatGPT to build your own quant investment strategy.

This course teaches the process of [idea generation - data collection - verification through backtesting] for developing a Bitcoin cryptocurrency coin bot that can generate automatic profits, as well as the principles of the AIS1, AIS2 investment strategy developed by the instructor, stock selection and buying and selling points and backtesting, and the source code for bot execution.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

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.

[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.

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

Innovation in Work Efficiency and Creativity – Agentic AI AI is now evolving beyond simple automation into a new stage where it can judge and execute on its own. Agentic AI is at the center of this change, serving as a core technology that helps businesses and organizations operate smarter. In this course, we have organized the content clearly and simply so you can understand how Agentic AI connects decision-making with execution and how it can be applied to practical work. Discover the essential knowledge for every professional preparing for the future right now.

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.

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.

A selfish era where only those who know how to properly utilize AI can generate profit. Essential web development concepts for profitability and a realistic guide you must know to 'properly' direct AI coding agents. From the mindset of coding without writing a single line of code to realistic methods for building a product with zero budget, and even practical details on business registration and tax processing. This is an AI-driven profitable web programming guide filled with insights gained from the actual field.

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

This is a 4-session intensive course designed to transform Claude from a simple conversation tool into a business automation system. You will learn core concepts such as agents, Claude Code, and workflows through practical use cases, gaining the hands-on ability to build end-to-end automation systems in your own field—even without development knowledge. This powerful weapon is most effective in the hands of non-developers who possess tacit knowledge. On the other hand, if you are a developer, it is good to understand why. An AI tsunami is approaching. Do not be swept away; use this as your weapon.

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.

AI agents, the latest trend in generative AI! How to stay ahead with Cursor, which handles all your development tasks, and MCP, which expands its capabilities!

Don't memorize commands. Just say, "Do this." Even without programming experience, let's have Claude Code perform 5 tasks. From file organization and document writing to data analysis, automation, and even web pages. The only payment required for this course is Claude Pro ($20/month). All other tools used are free.

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

If you follow OpenClaw Parts 1 and 2, you eventually end up making a single agent do everything. As you do this, there comes a moment when prompts get tangled, memory gets mixed up, folders get messy, and you feel stuck, not knowing how to organize things anymore. This lecture is the "Introduction to Multi-Agents (Part 2.5)," designed to help you move one step further from that point. While maintaining a single bot, we will separate roles into two agents—planner and editor—and connect each agent to different workspaces and Telegram forum topics. You will personally build a structure where "different agents respond depending on which room you speak in." By following the hands-on practice, you will have the following in your environment: - A configuration file defining two agents: `content_planner` and `content_editor`, - A content folder structure where planning and drafting are separated, - A Telegram group chat where different agents are attached to specific forum topics, - And one actual draft of an Inflearn course introduction created through the collaboration of the two agents. In short, this is a practical class that helps you design the first step of upgrading your Telegram bot from a "single AI assistant" into a "small AI team with divided roles."

It's okay if you don't know a single line of code. We are now in the era of 'Vibe Coding,' where you develop by 'conversing' with AI.

Building an MVP with AI is no longer difficult. However, most projects stop at the next stage. 👉 You’ve built the features 👉 But development doesn't continue Why does this happen? The problem isn't the code; 👉 It's because there is no structure that allows AI to work continuously. --- In this course, based on an existing project, 👉 We will cover the process of building a structure 👉 That allows AI to continue development on its own. --- Instead of simply using AI tools, we will: * Create a docs structure * Define the SSOT (Single Source of Truth) * Execute development on a per-ticket basis * Connect QA and iteration cycles 👉 To complete a single "AI Development System." --- Through this process, you will: 👉 Create a structure where development continues 👉 Even without a human manually coding. In other words, you can: 👉 Build and understand a development system 👉 That operates AI like a team. --- This course is designed for: 👉 Those who want to build a structure applicable to their projects immediately 👉 Those who started development with AI but found it difficult to sustain 👉 Those who want to move to the next level after "Vibe Coding" --- Beyond just learning, we provide an experience where you: 👉 Take action 👉 Build a structure that actually works 👉 And walk away with a framework you can apply to your own projects.

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.