Learning OpenAI Codex through Projects - From Basics to Advanced Vibe Coding Using AI
AISchool
Non-Majors Welcome: Real-World Vibe Coding Projects Created Through Conversations with AI
Beginner
Business Productivity, openai, codex
I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.
73 learners
Level Intermediate
Course period Unlimited
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!
We will learn step-by-step how to create AI agents using LangGraph while creating various practical AI agents.
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
👋 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. 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.
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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If you've taken the prerequisite course, the LangGraph course, this content is very easy to follow. In the prerequisite course, I learned well by referencing papers and implementing various architectures, but in this current course, the content is simple and focuses mainly on graphs, which felt a bit underwhelming. This current course is centered around clone projects that implement various AI services available on the market, which makes me think that the AI services out there are simpler than expected. If your goal is to study, I recommend the prerequisite course more, but if your goal is to easily and efficiently apply it directly in practice, this current course seems better! Also, while going through the course, there were quite a few parts where the instructor just read through the process of simply checking or comparing results, which felt inefficient from a learner's perspective. However, I was able to take the course by skipping those parts on my own and referencing the necessary sections well. Thank you for the great content!
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