LLM Data Analytics - From Web Crawling to Recommendation Systems

This is a basic Python course for non-major beginners. Theory textbooks are provided through Wikidocs, and practice materials are provided through Github.

Neo4J-based Knowledge Graph RAG System: Next-Generation Search Technology That Goes Beyond Simple Vector Search to Leverage Data Relationships Maximizing RAG Performance with the Power of Graph Databases!

This lecture covers RAG (Retrieval-Augmented Generation) system fundamentals and implementation using LangChain. Participants will understand RAG's core principles and learn to build and evaluate systems using LangChain.

Implementing a Retrieval-Augmented Generation (RAG) Intelligent AI Agent with LangGraph! From theory to practice, this is a hands-on tutorial that even beginners can easily follow.

Learn LangChain's basic concepts and usage with Python. Hands-on practice will be conducted primarily in a Google Colab (colab) environment, with materials provided via Github.

Create your own AI chatbot using Python's basic syntax and libraries. Learn through a step-by-step process of completing 5 projects, including PDF document-based RAG, and deploy them as web services.

Create your own AI chatbot using Python's basic syntax and libraries. Learn through a step-by-step process of completing 5 projects, including PDF document-based RAG, and deploy them as web services.

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

With easy explanations and various practical exercises, you can learn web crawling and website automation, and learn various applications such as website creation and ChatGPT usage.

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

Advanced Selenium course that is not blocked on large platforms Developing a bot that moves like a human

Learn about Python and how to automatically collect Naver news titles and stock data from Naver Finance using Python. The library I mainly use is BeautifulSoup.

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

Let's collect and refine data through public data, web scraping, and more, and perform exploratory data analysis!

Son Heung-min's 100th goal project! Practical crawling & data analysis of Transfermarkt

Let's use the R language, which processes data easily and conveniently, to develop computer program utilization skills related to one's major, even for non-computer majors!

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

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

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.

Simply by entering a US company ticker, this system automatically collects financial accounting data, competitor analysis, recent news, and stock price information, then performs in-depth analysis using the OpenAI GPT API to generate professional investment reports. The final results are automatically uploaded to Notion. By learning fundamental financial accounting concepts and terminology, even engineers can utilize this for competitor benchmarking, persuading supervisors, and planning new solutions. Build high-performance API servers with Python and FastAPI while learning asynchronous programming, AI integration, and practical fintech skills. Even beginners can easily follow along to complete a system applicable to real investment and business scenarios, helping them grow into developers suited for the AI era.

LLM: A pro quickly delivers core content, from essentials to practical tips!

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

Build basic R programming skills and learn basic crawling techniques. We will explain each line while writing the code yourself.