Mastering Model Context Protocol (MCP): Building Production-Grade AI Backends

This course is designed for developers, DevOps engineers, cloud engineers, and IT professionals who want to master advanced Kubernetes concepts used in real-world production environments. Throughout the course, students will engage in hands-on projects and real-world deployment scenarios, learning how to build secure, scalable, and production-ready Kubernetes infrastructure. Rather than focusing solely on theory, this course emphasizes practical implementation, troubleshooting, and best practices utilized by modern DevOps teams. One of the biggest challenges students face when learning Kubernetes is understanding how all components interact within large-scale environments. Many learners struggle with topics such as RBAC permissions, persistent storage, namespaces, network policies, StatefulSets, Kustomize, and secure cloud deployments. This course addresses these issues step-by-step through clear explanations, guided labs, and examples that mirror actual production environments. Students will learn step-by-step how to organize Kubernetes resources, secure workloads, manage application configurations, deploy stateful applications like MongoDB, and automate multi-environment deployments using Kustomize. Additionally, the course covers how to deploy applications to Google Kubernetes Engine (GKE) and configure Ingress, TLS certificates, monitoring, and scaling strategies. By the end of this course, students will have the confidence to design, secure, deploy, and manage advanced Kubernetes environments used in professional DevOps and cloud engineering workflows.

This course is designed for those who want to go beyond simple AI chatting and actually get work done using AI agents. Many students are already using ChatGPT or Claude, yet they still struggle with repetitive tasks, scattered workflows, excessive research, and low productivity. The problem usually doesn't lie with the AI itself, but in knowing how to organize work systematically, give clear instructions, and build reliable workflows. In this course, we provide a step-by-step guide on the practical application of Claude Cowork. Rather than focusing solely on theory, we practice through practical examples such as project management, browser automation, meeting preparation, persistent memory systems, skills, plugins, and desktop control. This course explains everything in a simple and practical way so that even beginners can easily follow along. By the end of the course, students will understand how to utilize Claude Cowork as a true productivity assistant for research, planning, organization, automation, and daily tasks.

This course is a practical, hands-on guide for those who want to learn how to maximize development productivity in the AI era. Beyond simply learning how to use AI, we focus on how to utilize Claude Code in a real-world development environment and how to produce "proper results." Many developers use AI but still face issues such as: ❌ Inconsistent results ❌ Poor code quality ❌ Increasing project complexity To solve these problems, this course teaches you how to utilize AI as a "development partner" rather than just a tool through: ✔ Spec-based workflows ✔ Structured command systems ✔ Sub-agent and automation strategies Furthermore, you will gain the experience of completing and deploying a full application from start to finish by integrating with real-world services such as Firebase, Figma, GitHub, and Vercel.

This course is designed to help learners build solid AWS cloud engineering and architecture skills through hands-on projects and practical demonstrations. Students will learn how to build secure AWS environments, manage scalable computing infrastructure using EC2 and Auto Scaling, configure networking via VPC and Load Balancers, and implement cloud-native storage and database solutions using S3, Aurora, and MySQL. Throughout the course, I will guide students step-by-step using hands-on labs, architectural explanations, and real-world deployment scenarios commonly used in modern cloud environments. I focus on simplifying complex AWS concepts into easy-to-understand lessons while helping students avoid common mistakes made during cloud deployment, security configuration, and infrastructure scaling. By the end of this course, students will not only gain the confidence to design secure, scalable, and cost-effective solutions on AWS but will also be prepared for the AWS Solutions Architect Associate certification exam.

This course is designed for developers, AI enthusiasts, and professionals who want to develop real-world AI applications using LangChain, OpenAI, Hugging Face, and Python. Rather than focusing solely on theory, this course takes a practical, project-oriented approach. Throughout the course, students will directly build a total of 15 high-quality AI applications, including chatbots, CSV analysis tools, resume screening systems, invoice extraction bots, support assistants, marketing tools, and text-to-SQL applications. I created this course to make complex AI concepts easy to understand and to ensure modern LLM development is accessible to everyone. Many learners struggle to understand how tools like LangChain, embeddings, memory systems, vector databases, and AI agents interact in real-world applications. This course guides you step-by-step through clear explanations, hands-on coding sessions, and actual implementation workflows. By the end of the course, students will confidently understand how to design, build, and deploy AI-powered applications using the latest generative AI technologies.

While studying Spring Security, you've probably wondered at least once: "I've built the login, but how do I handle authorization?" "How are authentication and authorization implemented in real-world practice?" This course is a practical, project-oriented lecture designed to solve those exact concerns. Instead of just explaining concepts, we implement them exactly as they are used in real services by: 👉 Building on an actual TodoApp project 👉 Integrating with a database 👉 Managing user permissions 👉 Handling validation In particular, I will provide easy, step-by-step explanations for the parts many students find difficult: Method-level authorization Access control within templates Validation using regular expressions Through this course, I will help you confidently say, 👉 "I can actually use Spring Security." 🚀

This course goes beyond simple grammar explanations and focuses on developing C++ programming skills that can be applied immediately in the field. Throughout my teaching, I have directly addressed common challenges faced by many learners—such as struggling to understand the STL or feeling confused by file handling and exception handling concepts. Based on this experience, I provide step-by-step explanations to ensure even complex concepts are understood easily and clearly. Furthermore, this course is not just about delivering theory; it is structured around: 👉 "Why this concept is necessary" 👉 "How it is used in actual code" 👉 "How it is applied in professional practice"

This course is designed to help even those new to MySQL understand it easily, starting from the basic concepts of DBMS and progressing step-by-step to the ability to write SQL that can be applied in real-world practice. To solve the problems learners frequently encounter (e.g., SQL syntax is difficult, not knowing how to write queries, or not understanding the flow of data processing), the course is structured with repetitive theory and practice, helping you naturally build your skills through a hands-on approach. In particular, the focus is not on simple memorization of syntax, but on understanding "why it is written this way" through the process of handling actual data.

This course explains everything from the basics of MySQL to its practical application in a step-by-step manner, making it easy to understand even for those learning databases for the first time. Rather than just providing simple theoretical explanations, the course focuses on real-world problem scenarios and teaches you how to write SQL and handle data through hands-on practice. The curriculum is systematically structured to cover the entire process, from DBMS concepts to MySQL installation, basic SQL syntax, data manipulation, and the use of functions. In particular, the goal is to help you understand "why it is written this way" through repetitive practice and to develop the ability to write queries on your own.

This is an introductory course designed for those learning Rust for the first time, structured to help you learn step-by-step from the basics. Many learners encountering Rust for the first time often find it difficult due to: - Unfamiliar syntax - The burden of memory concepts - Differences from existing languages (C/C++) To solve these problems, this course helps anyone understand Rust naturally through: 👉 Easy explanations 👉 Step-by-step curriculum 👉 Repetitive practical examples Rust is a modern systems programming language that offers both safety and performance, making it a very important technology in the future development landscape. Build a solid foundation of Rust's core basics through this course.

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 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.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

We understand why Attention was needed and how it works by 'implementing it directly with code'. This lecture starts from the structural limitations of RNN and Seq2Seq models, experimentally verifies the information bottleneck problem and long-term dependency issues created by fixed context vectors, and naturally explains how Attention emerged to solve these limitations. Rather than simply introducing concepts, we directly confirm RNN's structural limitations and Seq2Seq's information bottleneck problems through experiments, and implement **Bahdanau Attention (additive attention)** and **Luong Attention (dot-product attention)** one by one to clearly understand their differences. Each attention mechanism forms Query–Key–Value relationships in what way, has what mathematical and intuitive differences in the weight calculation process, and why it inevitably led to later models naturally connects to their characteristics and evolutionary flow. We learn how Attention views sentences and words, and how each word receives importance weighting to integrate information in a form where formula → intuition → code → experiment are connected as one. This lecture is a process of building 'foundational strength' to properly understand Transformers, helping you deeply understand why the concept of Attention was revolutionary, and why all subsequent state-of-the-art NLP models (Transformer, BERT, GPT, etc.) adopt Attention as a core component. This lecture is optimized for learners who want to embody the flow from RNN → Seq2Seq → Attention not through concepts but through code and experiments.

This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.

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.

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

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

You will learn the concepts of ontology and their practical application methods, focusing on the secondary battery cathode material manufacturing process. You will learn how to transform fragmented process data into meaningful knowledge assets and build AI-based quality prediction and defect root-cause reasoning systems. You will acquire practical ontology engineering capabilities to lead the digital transformation (DX) and artificial intelligence transformation (AX) of manufacturing sites.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

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!

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!

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.

Create a program using the GPT API, and even generate images and videos!

[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.

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."

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!

AI agents, the latest trend in generative AI! How to stay ahead with Cursor, which handles all your development tasks, and MCP, which expands its capabilities!