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Generative AI

LLM Data Analytics - From Web Crawling to Recommendation Systems

This course is designed for beginners and consists of easy explanations and various hands-on projects. It covers data collection using web crawling and LangChain tools, and summarization, extraction, sentiment analysis, and recommendation systems using LLM.

(4.4) 5 reviews

91 students

LLM
LangChain
Web Crawling
Web Scraping
Recommendation System
Thumbnail

This course is prepared for Basic Learners.

What you will learn!

  • Data collection using web crawling/scraping

  • Collect, refine, and analyze data using LangChain tools and LLM

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

Upgrading to LangChain and LLM
Web Crawling & Data Analysis 🚀

This course is designed to help beginners easily learn web crawling, data collection using LangChain tools, and data analysis techniques using LLM. You will learn how to collect necessary data from the web and analyze it to derive business insights.

The course covers web crawling/scraping training using BeautifulSoup and Selenium, as well as data collection and analysis techniques using LangChain and LLM. We will directly collect real-time news data, YouTube product review and comment data, and ETF fund data, and implement summary, extraction, sentiment analysis, and recommendation systems using LLM.

Pagination and collection with Selenium

Extract and summarize product information from YouTube product review videos

Python Web Crawling Basics + LangChain (LLM) Application

Lecture Features ⭐

1⃣ Project-based, hands-on learning

Learn how to perform web crawling on real websites. Select real-time news categories from the portal and collect them, perform text summaries, keyword extraction, and entity information extraction. Then, analyze YouTube product review videos and create a US ETF fund recommendation system.

Screenshot 2024-04-27 145811

YouTube product review video analysis

2⃣ Applying the latest LLM techniques utilizing LangChain

Learn traditional web crawling techniques first, then cover data collection techniques using LangChain tools and data analysis techniques using LLM. The difficulty level is adjusted step by step so that beginners can easily follow. (Python and LangChain lectures for beginners are provided for free.)

Langchain Basics

LangChain offers free lectures

3⃣ Provide updates when website changes occur

For web crawling examples, if the website configuration or source code changes, it may not run as is. We will periodically check the lecture videos and practice codes to provide updates.

Screenshot 2024-04-27 151543

ETF fund portfolio composition screen

Things to note before taking the class

Practice environment

  • Operating System and Version (OS): Lecture will be based on Windows (Linux and MacOS users can also practice)

  • Tools used: Miniconda, Jupyter Lab, OpenAI API authentication key required (separate costs may apply)

  • PC specifications: PC or laptop with internet access

Learning Materials

Player Knowledge and Notes

  • Those who have basic knowledge of Python (Free Python lectures available: link )

  • Basic understanding of LLM and LangChain (free lectures available: link )

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

Linked lecture guide (1)

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

  • From RAG implementation to performance evaluation -

    Practical AI Development Completed in 9 Hours

    • LangChain-based RAG system construction practice

    • Learn advanced RAG techniques

    • RAG System Performance Evaluation Methodology

    • LangChain's latest LCEL grammar and how to use Runnable


  • Link: https://inf.run/mdYe4

Linked lecture guide (2)

  • Creating Python Chatbot & RAG through Projects - Using LangChain, Gradio

  • Consists of a total of 4 projects


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

    • PDF-based RAG Chatbot: Understanding RAG Techniques, Understanding Model Parameters, Implementing Chatbot Interface

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

    • Investment Analyst Chatbot: Cryptocurrency Research and Investment Analysis (Multi Agent)

  • Link: https://inf.run/PfJaS

Linked lecture guide (3)

  • RAG system implemented with AI agent (w. LangGraph)

  • Intelligent AI agent for augmented search generation (RAG) implemented with LangGraph


    • Design and implementation of AI agent structure using LangGraph

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

    • Expanding the capabilities of AI agents by implementing Tool Calling functionality

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

  • Link: https://inf.run/hTwjC

Recommended for
these people!

Who is this course right for?

  • People who want to collect data needed on a web page

  • Those who want to learn data analysis techniques using LLM

  • Those who want to learn how to learn and apply Python

  • Those who like project-based, practice-centered lectures

Need to know before starting?

  • Python Basics

  • Basic knowledge of LLM concept (not required, recommended)

Hello
This is 판다스 스튜디오

Students

2,848

Reviews

168

Rating

4.8

Courses

6

안녕하세요. 저는 파이썬을 활용한 데이터 분석 및 인공지능 서비스 개발 실무를 하고 있습니다. 관심 있는 주제를 찾아서 공부하고 그 내용들을 많은 분들과 공유하기 위해 꾸준하게 책을 집필하고 인공지능 강의를 진행해 오고 있습니다.

 

[이력]

현) 핀테크 스타트업 CEO

전) 데이콘 CDO

전) 인덕대학교 컴퓨터소프트웨어학과 겸임교수

Kaggle Competitin Expert, 빅데이터 분석기사

 

[강의]

NCS 등록강사 (인공지능)

SBA 서울경제진흥원 새싹(SeSAC) 캠퍼스 SW 교육 ‘우수 파트너 선정’ (Python을 활용한 AI 모델 개발)

금융보안원, 한국전자정보통신산업진흥회, 한국디스플레이산업협회, 대구디지털산업진흥원 등 강의

서울대, 부산대, 경희대, 한국외대 등 국내 주요 대학 및 국내 기업체 교육 경험

  

[집필]

 

[유튜브] 판다스 스튜디오 : https://youtube.com/@pandas-data-studio?si=XoLVQzJ9mmdFJQHU

Curriculum

All

29 lectures ∙ (3hr 10min)

Lecture resources

are provided.

Published: 
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