Mastering Model Context Protocol (MCP): A Practical Guide -(2026)

This course is a practical, hands-on guide to automating repetitive tasks and maximizing work productivity using ChatGPT and Python. You will learn step-by-step how to use AI to automate daily repetitive tasks such as Excel work, report writing, sending emails, and data organization. Starting as a non-developer myself, I have taught AI automation to thousands of students, and I have compiled only the methods that can be used immediately in actual business environments. This course focuses not on complex theories, but on **"automation techniques you can use right today."** 👉 The goal is to turn ChatGPT into more than just a tool, but into **your own personal work assistant (Jarvis)**.

This is an introductory course for understanding the concept and application methods of Vision-Language Models (VLM), and practicing running the LLaVA model in an Ollama-based environment while integrating it with MCP (Model Context Protocol). This course covers the principles of multimodal models, quantization, service development, and integrated demo development, providing a balanced mix of theory and hands-on practice.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

Do you want to automate repetitive tasks with AI without using paid tools? I will share the practical know-how I gained from building systems myself using n8n and Llama. This is not a lecture where you simply learn "how to connect nodes." We will build actual workflows that you can use immediately, such as automatic Google Sheets organization, YouTube subtitle collection, AI fact-checking, and schedule management.

Chatgpt is a program that reads and comments on posts written by my neighbors Lesson on developing marketing automation and neighbor management programs

This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

It's finally here. The world's easiest machine learning course. It's free, so why hesitate? Just keep pressing the 'Learn' button.

During the Gold Rush, it was said that it was easier to become rich by selling gold mining tools than by mining gold yourself. How about selling programs in the ChatGPT Age of Exploration?

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

Python Korean Text Analysis and Natural Language Processing: Word Cloud Visualization, Morphological Analysis, Topic Modeling, Clustering, Similarity Analysis, Bag of Words and TF-IDF for Text Data Vectorization, Text Classification Using Machine Learning and Deep Learning, and How to Use Hugging Face

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

Build a fully functional smart farm platform that integrates sensor data collection, MQTT communication, web dashboards, and AI-driven environmental analysis. Experience the entire process of agricultural digital transformation (DX) through a hands-on project, covering everything from IoT sensor integration and FastAPI/Spring-based API servers to React/Vue dashboard development and cloud deployment.

Build your own powerful stock price analysis model that analyzes over 40 types of economic indicators and stock price data using AI to predict not only the S&P 500 and QQQ ETFs but also individual stocks!

Automate YouTube Shorts creation with AI!! Learn how to automatically create YouTube Shorts using n8n from scratch. We will personally build a workflow that handles everything at once: Text → Image/Music → Video Creation → Uploading. Practice includes distinguishing between test/production modes and tips for saving costs. By the end of the lecture, you will complete 3 automation templates that can be used immediately. We will learn step-by-step, starting from node placement, so you can follow along even without coding knowledge. Join us in automating AI YouTube Shorts production!

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

The goal is to master the complex GPT API and Python in the easiest way possible through hands-on practice. You will develop a YouTube video summarization AI using the latest ChatGPT API and implement it as a web application using Streamlit.