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AI Agent Development

Large Language Models for All LLM Part 6 - Implementing AI Agents Using LangGraph Through Projects

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

(3.3) 3 reviews

53 learners

  • AISchool
ai활용
ai프로젝트
실습 중심
LangGraph
AI Agent
LangChain
openAI API
RAG

What you will learn!

  • How to Implement an AI Agent with LangGraph

  • How to Implement Various Practical AI Agents

  • Practical Use Cases of AI Agents

  • Various AI Agent Architectures

AI Agent, the Megatrend in the Tech Industry!
Learn how to implement practical AI agents through a variety of projects!

By creating various AI agents through the project,
Let's learn how to implement practical AI agents using LangGraph !

We will learn step-by-step how to create AI agents using LangGraph while creating various practical AI agents.

  • ✅ Learn how to implement AI agents using the LangGraph library.
  • ✅ Learn how to implement AI agents through various projects.

Introducing the implementation project 😊

AI News Service - Translation and Summary of Overseas News
Using AI to crawl overseas news articles, translate and summarize them into Korean.
We perform keyword extraction, sentiment analysis, etc. and evaluate performance.

YouTube Summary Service - YouTube video translation and summary
Using AI, we crawl YouTube video scripts and translate them into Korean.
We will summarize the content and evaluate the performance.

Naver Blog Post Creation Service - Automated Blog Writing
After creating a table of contents for Naver blog posts using AI,
Run automated blog posts and evaluate their performance.

Market Summary Service - Summary of Key Stock Market Information
Using AI to crawl key information from the stock market
We summarize and visualize the results and evaluate the performance.

Who is this course for?

Anyone who wants to create a practical AI agent

Anyone who wants to create their own AI agent using LangGraph

Anyone who wants to improve their LangGraph implementation skills

Anyone who wants to develop a service using the latest LLM model


Player Course ✅

👋 This course requires prior knowledge of Python, Natural Language Processing (NLP), LLM, LangChain, and LangGraph . Be sure to take the courses below first or have equivalent knowledge before taking this course.


Q&A 💬

Q. What are the benefits of learning how to implement AI agents using LangGraph through projects?

LangGraph is a powerful framework that allows for the flexible construction of complex AI agents , and has recently attracted attention as a key tool for AI agent development.

Learning LangGraph on a project-by-project basis has the following advantages: 

1. Practice-oriented learning :

Rather than simply learning theories, you can gain practical experience by creating AI agents that actually work. You can build capabilities that can be applied directly to the field.

2. Experience in designing complex agent logic :

LangGraph allows you to visually and clearly structure complex logic, such as multi-step inference, branching, and stateful flows. This will help you develop the ability to design and implement advanced agents.

3. Expanding understanding of the LangChain ecosystem :

Since LangGraph operates based on LangChain, you can naturally learn the core concepts of LangChain and how to utilize various tools.

4. Acquire the latest technology trends :

AI agents are a core technology that will be applied to various services in the future. LangGraph is a tool that is rapidly spreading in this flow, and learning it in advance can increase your competitiveness.

5. Can be used as a portfolio :

The results created through the project can be used as your own portfolio, becoming a powerful weapon when seeking employment or changing careers.

Q. Is player knowledge required?

This lecture [ Large-Scale Language Model for Everyone LLM Part 6 - Implementing AI Agents Using LangGraph through Projects ] covers a project practice of implementing AI agents using the LangGraph library and LLM . Therefore, the lecture proceeds under the assumption that you have basic knowledge of Python, natural language processing, LLM, LangChain, and LangGraph. Therefore, if you lack prior knowledge, please be sure to take the preceding lecture [ Large-Scale Language Model for Everyone LLM Part 5 - Build Your Own AI Agent with LangGraph ] first.

Recommended for
these people

Who is this course right for?

  • For those who want to create their own AI agent using LangGraph

  • For those who want to find a job in deep learning research.

  • Anyone interested in conducting research related to AI/deep learning

  • Someone preparing for AI graduate school

  • Anyone who wants to implement practical AI agents

Need to know before starting?

  • Experience with Python

  • Course Review: [Large Language Models (LLM) for Everyone Part 5 - Building Your Own AI Agent with LangGraph]

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Curriculum

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37 lectures ∙ (7hr 27min)

Published: 
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3 reviews

3.3

3 reviews

  • 멍멍이망고님의 프로필 이미지
    멍멍이망고

    Reviews 5

    Average Rating 5.0

    Edited

    5

    100% enrolled

    선수강의인 LangGraph 강의를 수강했다면 정말 가볍게 볼 수 있는 내용입니다. 선수 강좌에서는 논문을 참고하며 다양한 아키텍처를 구현하면서 학습이 잘 됐는데, 오히려 현 강의에서는 간단한 그래프 위주의 내용이어서 김이 조금 샌 부분이 있습니다. 현 강의는 시중에서 제공되는 다양한 AI서비스를 따라서 구현하는 클론 프로젝트 중심인데, 그만큼 시중에 있는 AI 서비스가 생각보다 간단한 것이라고 생각해도 될 것 같네요. 공부가 목적이라면 선수 강의를 더 추천하고, 실무에 쉽고 효율적으로 바로 써먹는 것이 목적이라면 현 강의가 더 좋아보이긴 합니다! 그리고 강의를 진행하시면서 결과를 단순히 확인하거나 비교만 하는 과정에서 쭉 읽어나가시기만 하는 부분이 꽤 많았는데, 학습하는 입장에서는 비효율적으로 느껴졌습니다. 그래도 그런 부분은 알아서 스킵하면서 필요한 부분 잘 참고하면서 수강 했습니다. 좋은 내용 감사해요!

    • 용용용!!님의 프로필 이미지
      용용용!!

      Reviews 10

      Average Rating 4.5

      4

      60% enrolled

      • 교육님의 프로필 이미지
        교육

        Reviews 1

        Average Rating 1.0

        1

        97% enrolled

        화질이 안 좋아요.

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