LangChain Basics for Beginners — v1.0+ Update

An introductory course that covers the core of LLMs, Agents, and RAG using LangChain v1.0+'s simplified API and free LLMs (Gemini · Groq · Ollama), all without the burden of credit card registration.

(4.8) 414 reviews

5,962 learners

Level Basic

Course period Unlimited

Python
Python
LLM
LLM
LangChain
LangChain
RAG
RAG
AI Agent
AI Agent
Python
Python
LLM
LLM
LangChain
LangChain
RAG
RAG
AI Agent
AI Agent
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Reviews from Early Learners

4.8

5.0

안주현

43% enrolled

Thank you for the great lecture.

5.0

Baek-Kwangho

71% enrolled

It's so great for viewing easily.

5.0

컴공과

57% enrolled

It was great because it was structured to be easily understood through basic concepts and practical code. Also, explaining the flow of the code with a touch pen made it a lecture where I could perfectly understand the code analysis.

What you will gain after the course

  • Understanding the Core Structure of LangChain v1.0+ (init_chat_model, LCEL, Runnable)

  • Structured Output and Pydantic Utilization

  • Implementing AI Agents based on Tool Calling and LangGraph (create_agent)

  • Building a RAG pipeline (Embedding → FAISS → Retrieval → Generation)

  • How to Use Free LLM APIs (Google Gemini · Groq · Ollama)

LangChain Basics for Beginners

This is an introductory course on LangChain, the leading framework for developing LLM applications easily and conveniently.





What you will learn

Basic Structure of LangChain

  • Learn the basic concepts and usage of LangChain through hands-on practice.

  • The latest stable version (v0.1.10) is applied.

LangChain v0.1.1*

RAG(Retrieval-Augmented Generation)

  • We will study RAG (Retrieval-Augmented Generation), a representative technique for preventing hallucination in LLM-based generative AI models.

Google Colab practice environment

Notice for Existing Students

📢 To existing students

This update is a completely re-recorded version of the entire course to align with the major changes in LangChain v1.0. The existing v0.x videos can still be viewed in a separate section (Archive), and the new 12 lessons are available within the same course.

Key Changes:

  • Expanded from 7 lectures → 12 lectures (6 Theory + 6 Practice)

  • OpenAI Paid API → Gemini Free API Default Use

  • New chapters added: LangGraph-based Agents · Tool Calling · Structured Output

Notes before taking the course

Practice Environment

  • Operating System and Version (OS): Windows / macOS / Linux are all supported

  • Tools used: VS Code (local, recommended) or IDE, Python 3.12 or higher, one free API key required (Google Gemini recommended — no card registration required / or choose one from Groq, Ollama, OpenAI)

  • PC Specifications: Standard office PC is sufficient (8GB RAM or higher recommended when using local Ollama models)

  • (Must Read!) Practice Environment Setup Guide:https://github.com/pandas-studio/langchain-basic-course/blob/main/SETUP.md

Learning Materials

Prerequisites and Notices

  • Those who have basic knowledge of Python and an understanding of machine learning


  • This course does not cover Python syntax or the principles of artificial intelligence.

  • If you have any questions or opinions, please feel free to ask.


Related Course Information (1)

  • RAG Master: From Basics to Advanced Techniques (feat. LangChain)

  • From RAG implementation to performance evaluation -

    Practical AI Development in 9 Hours

    • Hands-on Practice in Building a LangChain-based RAG System

    • Learning Advanced RAG Techniques

    • RAG System Performance Evaluation Methodology

    • Latest LCEL syntax of LangChain and how to use Runnable


  • Link: https://inf.run/CxVA3

Related Course Information (2)

  • Learning through Projects: Building Python Chatbots & RAG - Using LangChain and Gradio

  • Consists of a total of 4 projects


    • Simple QA Chatbot: Setting up the development environment, understanding LLM Chain structure and Gradio interface  

    • PDF-based RAG Chatbot: Understanding RAG techniques, understanding model parameters, and implementing a chatbot interface

    • Data Analysis Chatbot: Upload CSV files and analyze the corresponding data (Single Agent)

    • Investment Analyst Chatbot: Cryptocurrency research and investment analysis (Multi Agent)

  • Link: https://inf.run/SKi9z

Related Course Information (3)

  • LLM Data Analysis - From Web Crawling to Recommendation Systems

  • Upgrading with LangChain and LLM

    Web Crawling & Data Analysis


    • Data collection using web crawling/scraping

    • Data collection, cleaning, and analysis using LangChain tools and LLM

    • Predictive analysis using LLM (sentiment analysis, summarization, product recommendation, etc.)

  • Link: https://inf.run/JrSKR

Related Course Information (4)

  • RAG System Implementation with AI Agents (w. LangGraph)

  • Intelligent AI Agent for Retrieval-Augmented Generation (RAG) Implemented with LangGraph


    • Designing and implementing AI agent structures using LangGraph

    • Applying AI Agents to RAG (Retrieval-Augmented Generation)

    • Expanding AI agent capabilities by implementing Tool Calling functionality

    • Mastering the latest Agentic RAG architectures, including Adaptive RAG, Self RAG, and Corrective RAG

  • Link: https://inf.run/tkfVa

Recommended for
these people

Who is this course right for?

  • A beginner learning LangChain for the first time

  • Those who learned LangChain during the v0.x era but have not been able to keep up with the changes in v1.0

  • Those who want to practice generative AI for free without registering a card

  • Those who want to take their first step in AI Agent / RAG chatbot development

Need to know before starting?

  • Python

  • Machine Learning Basics

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This is pdstudio

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4.8

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Hello. I am currently working in the field of data analysis and AI service development using Python. I have been consistently writing books and delivering AI lectures to share the topics I study with as many people as possible.

[Experience] Current) CEO of a Fintech Startup Former) CDO at Dacon Former) Adjunct Professor, Department of Computer Software, Induk University Kaggle Competition Expert, Big Data Analysis Engineer [Lectures] NCS Registered Instructor

[Experience]

Current) CEO of a Fintech Startup

Former CDO at DACON

Former Adjunct Professor, Department of Computer Software, Induk University

Kaggle Competition Expert, Big Data Analysis Engineer

[Lectures] NCS Registered Instructor (Artificial Intelligence) Selected as an 'Outstanding Partner' for SBA (Seoul Business Agency) SeSAC Campus SW Education (AI Model Development using Python) Financial Security Institute, Korea Electronics

[Lectures]

NCS Registered Instructor (Artificial Intelligence)

Selected as an 'Outstanding Partner' for SW Education at the Seoul Business Agency (SBA) SeSAC Campus (AI Model Development using Python)

Lectures at Financial Security Institute, Korea Electronics Association (KEA), Korea Display Industry Association (KDIA), Daegu Digital Industry Promotion Agency (DIP), etc.

Experience in providing education at major domestic universities such as Seoul National University, Pusan National University, Kyung Hee University, and Hankuk University of Foreign Studies, as well as for domestic corporations

[Writing] Python Machine Learning Pandas Data Analysis (InfoBook): https://zrr.kr/x1ec Python Deep Learning Machine Learning Introduction (InfoBook): https://zrr.kr/RPaE Python Deep Learning Ten

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[YouTube] Pandas Studio : https://youtube.com/@pandas-data-studio?si=XoLVQzJ9mmdFJQHU

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414 reviews

4.8

414 reviews

  • jhjun809님의 프로필 이미지
    jhjun809

    Reviews 2

    Average Rating 5.0

    5

    100% enrolled

    It was a great introductory course for LangChain. Thank you!

    • pdstudio
      Instructor

      Thank you!

  • justindev님의 프로필 이미지
    justindev

    Reviews 6

    Average Rating 5.0

    5

    57% enrolled

    It was great because it was structured to be easily understood through basic concepts and practical code. Also, explaining the flow of the code with a touch pen made it a lecture where I could perfectly understand the code analysis.

    • pdstudio
      Instructor

      Thank you!

  • negroni085667님의 프로필 이미지
    negroni085667

    Reviews 1

    Average Rating 5.0

    5

    71% enrolled

    It's so great for viewing easily.

    • pdstudio
      Instructor

      Thank you!

  • identis716875님의 프로필 이미지
    identis716875

    Reviews 1

    Average Rating 5.0

    5

    43% enrolled

    Thank you for the great lecture.

    • pdstudio
      Instructor

      Thank you!

  • sunnylgcns님의 프로필 이미지
    sunnylgcns

    Reviews 2

    Average Rating 5.0

    5

    43% enrolled

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