Together with Professor Soon-chil Lee, South Korea's leading authority on quantum computing, you will systematically learn everything from the core principles of quantum technology to its business impact. Through the basic concepts of quantum mechanics, the operating principles of quantum computers, and industrial application cases, you will complete your quantum literacy to preemptively understand future technological changes and establish strategies.

Systematically learn the internal structure and operating principles of Apache Kafka, a core technology for real-time data streaming. From basic concepts to cluster construction, client development, and microservices application, you will acquire all the practical knowledge and know-how necessary to build the capability for stable Kafka operations.

We will examine the changes that AI technology has brought to the entire financial industry through various cases such as fintech, investment, credit risk, and fraud detection. Through hands-on practice using Python libraries, you will understand the application methods and strategic insights of financial AI and learn how to safely integrate generative AI into financial services.

Starting from installing MariaDB and understanding its structure, you will learn step-by-step from writing SQL queries and utilizing functions to joins and subqueries. You will master core practical features such as table design, performance management, backup, and recovery through hands-on practice, completing the practical skills necessary for database development and operation.

Learn how to efficiently manage the entire project process by applying the core principles of Agile methodology to Jira and Confluence. From prioritizing tasks to turning knowledge into assets, you will acquire practical collaboration tool skills to maximize project performance.

This is a practical clean code writing course for developers who have completed the basics of Python. You will learn specific methodologies for writing highly readable and maintainable code, and develop coding skills that can be immediately applied to real-world projects through the know-how of professional programmers.

Systematically learn Docker, a core tool for container-based application deployment, from the basics to practical application. Starting with Docker's basic structure and commands, you will develop practical skills that can be immediately applied to real-world tasks, including container packaging in microservices architecture environments, deployment automation, and standardization of production environments.

Learn how to develop cloud-native applications using Kubernetes, the standard for container orchestration. From core Kubernetes concepts to actual application deployment and operations automation, you can acquire practical skills that are immediately applicable in the field. Learn how to build efficient resource management and deployment processes by integrating development and production environments.

This is a process of developing a practical web service in collaboration with AI agents based on TARS prompt engineering principles. You will gain full-stack development experience covering everything from UI/UX component creation to CRUD functions, API integration, and data visualization, ultimately completing your own idea as a viable MVP.

In an era where flexible data processing beyond the limits of SQL is required, learn everything about MongoDB. From NoSQL concepts to installation, CRUD practice, aggregation pipelines, text search, and indexing, you will learn step-by-step and complete your practical skills through Python crawling integration and container environment construction.

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

This course is designed for those who are new to React, an essential skill in frontend development. We break down difficult concepts into easy-to-understand terms, allowing you to naturally master everything from the basic principles of React to practical application through hands-on learning. By addressing the common pain points where beginners often get stuck and providing project-based practice, we help you grow into a confident React developer.

This is a course on building your own stock and cryptocurrency automated trading programs using ChatGPT, even without prior coding knowledge. You will gain both AI development skills and financial investment knowledge through hands-on practice covering the entire process, from data collection to predictive model development, stock recommendation systems, and actual automated trading.

Learn how to reduce marketing risks through AI and efficiently execute the entire process, from branding and personalized marketing to content creation. From the basics of prompt engineering to practical application, we present essential AI utilization methods for marketing professionals.

You will systematically learn the principles and practical applications of the latest generative AI technologies, from ChatGPT to image and video generation AI and no-code tools. Through seven core topics, including prompt engineering, Copilot utilization, and AI extensions, you can maximize work efficiency and develop creative problem-solving skills.

We explain the operating principles of complex IT systems, such as servers, networks, clouds, and AI infrastructure, using simple everyday analogies. Even non-experts can systematically learn the core literacy needed to communicate smoothly with technical experts and make logical decisions in a digital environment.

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

Explore how LLM applications are shifting the development paradigm. Understand why LLMs are becoming a sustainable core technology through comparisons with existing technologies, and learn about the essential skills and mindset shifts required for developers. Gain insights from expert instructors to grasp the essence and future direction of LLM application development.

[Understanding AI Foundation Models and Their Operating Principles: Engineering Control and System Architecture, Practical Methodologies for Resolving AI Uncertainty and Engineering Assetization] 1. Introduction: The Necessity of Engineering Control of Intelligence (Engineering Control vs. Systemic Chaos) A core conclusion derived from long-term practical insights in industrial fields is that power that is not properly controlled acts as a potential liability rather than an asset. Even a high-performance engine is nothing more than an unstable physical mass if it lacks sophisticated combustion logic and microsecond-level control systems. The organizational chaos currently appearing in the process of adopting Generative AI is judged to stem from a lack of understanding of these control principles and a blind faith in technical "black boxes." This masterclass redefines Artificial Intelligence not as a mysterious stochastic phenomenon, but from the perspective of Model-Based Engineering (MBE). By transforming the uncertain domain of intelligence into a predictable and reliable engineering framework, we aim to present a strategic methodology that allows organizations to secure strong leadership across the entire system without being dependent on technical trends. 2. The 4 Pillars for Solving Core Challenges ① Epistemological Paradigm Shift: Visualizing the Black Box and Assetizing Technical Debt Many companies are facing "technical debt"—characterized by exposure to security vulnerabilities and exponential increases in maintenance costs—by adopting AI models without a clear understanding of their internal structures. This course assetizes this through the following approaches: Deconstruction of Mechanisms: We engineeringly deconstruct the Self-Attention mechanism, the core of the Transformer architecture, from the perspective of numerical weight analysis. By understanding the numerical mechanisms that determine information priority, we visualize the basis for the model's judgment. Analysis of ID Formation: We transparently track the process by which a series of pipelines—leading from Pre-training to Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)—forms the model's technical identity and ethical guidelines. This converts invisible threats into controllable system parameters. ② Securing Deterministic Reliability: Hallucination Control Strategies to Overcome Probabilistic Limits Large Language Models (LLMs) are not systems that reason for truth, but systems that generate the next most probable token. The "hallucination" phenomenon resulting from this inherent characteristic becomes a fatal flaw in engineering fields where reliability is vital. Constraints of Retrieval-Augmented Generation (RAG): We move away from closed structures that rely solely on the model's internal fixed memory (Internal Weights). We establish an "open-book strategy" that provides clear grounding for generated results by allowing real-time reference to trusted external knowledge bases. Hybrid Model Architecture: We design a redundancy strategy that achieves both accuracy and operational efficiency by deploying large models for areas requiring enterprise-wide knowledge and optimized Small Language Models (SLMs) for specific domains where security and real-time response are essential. ③ Computing Architecture Optimization: Overcoming Physical Bottlenecks (Memory Wall) While intelligence is implemented in software, its performance and economic sustainability are defined by the physical limits of hardware. Physical Constraint Analysis: We diagnose the "Memory Wall" problem, where data transfer speeds cannot keep up with the processing speeds of computing units, and heat generation issues resulting from high-density computation from an engineering perspective. Infrastructure Design Capability: We precisely analyze the physical impact of High Bandwidth Memory (HBM) stacking structures and 2.5D/3D advanced packaging technologies on inference efficiency. We cultivate design capabilities to optimize Total Cost of Ownership (TCO) through full-stack integrated insights that complement hardware limitations with software architecture. ④ Acceleration of Functional Expansion: Transitioning from Passive Tools to Autonomous Agent Systems Current AI remains at the level of simple Q&A, failing to create added value for practical business automation. This course evolves AI into an active subject that judges and executes on its own. Decomposition: We learn techniques for decomposing complex goals into achievable sub-tasks and logically organizing the execution sequence upon receiving them. Digital Workforce Deployment: We define the process of applying "Active Agent" systems to the field, which autonomously call internal ERPs, browsers, and external APIs to complete actual business logic and accept feedback on results. 3. Core Architecture: Closed-loop Control System The way an AI agent manifests intelligence and performs complex tasks is theoretically identical in logical structure to the closed-loop control system performed by an ECU (Electronic Control Unit), the core brain of a car. This course analyzes this in detail from the perspective of the ReAct (Reasoning and Acting) framework. First, the system begins at the Input stage, receiving ambiguous and complex requests from the user. This plays the same role as a sensor in control engineering collecting physical data from the external environment and delivering it to the system, serving as the standard for defining the initial state of the task at hand. Second, based on the received data, the Thought stage proceeds, where plans are established through logical reasoning within the LLM architecture. This is in line with the process where control algorithms in an ECU calculate optimal control values by processing input sensor data. At this stage, the agent sets the optimal path to achieve the goal and secures the logical rigor of the system. Third, the Action stage follows, where tasks are completed by calling external tools or APIs according to the established plan. This logically matches the mechanism where the calculation results of a control system are converted into physical power through an actuator to execute commands. Through this, intelligence exerts actual physical and digital influence beyond abstraction. Finally, the Observation stage is performed, analyzing the execution results and correcting errors relative to the initial goal. This is identical to the core principle of control engineering, which reduces system deviation through a feedback loop. The agent self-verifies whether the execution results meet the goal and continuously upgrades performance by reflecting occurred errors into the next action plan. AI equipped with such a closed-loop structure is no longer an incomplete system dependent on probability. By securing engineering rigor that self-verifies execution results and corrects errors, it functions as a trust-based partner capable of performing business-critical tasks. 4. Practical Application and Expansion: Software-Defined Vehicles (SDV) and Physical AI The final destination of AI architecture lies in the cross-industry proliferation of Software-Defined Vehicles (SDV) and Physical AI, which overcome and evolve physical constraints through software intelligence. This is the standard model for future System Integration (SI) across manufacturing and service industries. Securing Edge Intelligence and Data Sovereignty: Small models (SLMs) mounted on-device in vehicles or facilities learn real-time field data immediately. This minimizes cloud dependency, perfectly protecting data sovereignty—a core asset of the company—and enables precision services based on ultra-low latency. Hardware Optimization and Lightweight Engineering: To implement the best intelligence within limited power and computational resources, we actively introduce model compression technologies such as Quantization, Pruning, and Knowledge Distillation. Model deployment considering hardware bandwidth becomes a core competency that determines system response speed and user experience. Hybrid Orchestration: We design an integrated architecture that organically connects "Cloud LLMs" possessing broad general knowledge with "Edge SLMs" specialized for specific physical control and security. Integration from a full-stack perspective, penetrating from silicon chipsets to software stacks, provides a powerful competitive advantage that evolves the entire system through software updates alone. 5. Conclusion: The Role and Vision of the AI Architect The ultimate goal of this masterclass is to elevate students from the position of a "User" who passively relies on technology and hopes for luck, to a professional "AI Architect" who perfectly controls and tunes everything from the physical limits of the system to the depths of the software architecture. While the phenomenon of intelligence manifests from software logic, it is silicon (hardware) that defines the physical limits of that intelligence, and only sophisticated engineering can overcome those limits to complete actual business value. "Intelligence may reside in the realm of probability, but the vessel that contains that intelligence and makes it operate according to purpose must belong solely to the realm of rigorous and sophisticated engineering."

Make your own AI autonomous vehicle! This is a course where you can learn about AI and reinforcement learning in the most fun and fastest way. This course was produced by the developer ranked #1 in DeepRacer Korea.

🚨 "Automatic control? Oh, that headache-inducing stuff?" If that was your reaction, come on in! Does the mere mention of "automatic control" give you a throbbing headache? Don't worry, that's perfectly normal! LOL. To be honest, I was the same way. Casting my mind back 17 years to my rookie days when I didn't know the first thing about automatic control... I suddenly landed a job at a foreign company dealing with factory automation systems. My naive fantasy that "the company isn't a school, so seniors will kindly spoon-feed me everything" was shattered into pieces. Expecting a 'kind mentor' from seniors whose souls were being drained by a daily deluge of work was a luxury. In the end, I had to learn the hard way by digging into thick manuals on my own, grasping concepts one by one through blood, sweat, and tears. The result of enduring and grinding through it all? I grew into a seasoned 18-year veteran engineer, leading HVAC construction and commissioning for major overseas projects like Kuwait Oil Company, Abu Dhabi National Oil Company, and Saudi Aramco. ✌️ 💡 Take the shortcut with the know-how I gained from my struggles! I poured everything into this course for juniors and seniors who, like me, are full of passion to grow but feel lost on where and how to start. I'll be honest. I won't lie and say, "Take this one course and you'll be an automatic control master tomorrow!" However, I am confident that once you finish this course, you will have a more solid and clear foundation than anyone else regarding "Ah, so this is what automatic control is!" and "This is how all the gears mesh together!" 🌐 Regardless of your major, automatic control is now a 'must-have survival item'! AI, IoT, autonomous driving, smart factories... the world is changing so fast these days, right? We've reached an era where it's hard to work properly without an understanding of automatic control. For electrical engineers, monitoring systems like EPMS and SCADA are basic setups! Mechanical engineers have also become inseparable from control systems like BMS and FMS. Data center engineers? They have to handle systems with dizzying names like EPMS, BMS, DCIM, and DCOS. While we can't perfectly dissect these complex-looking systems from 1 to 100, I will use my 18 years of experience to explain the **'core functions' and 'basic structures'** they must possess in a way that is far more intuitive and easy to grasp than anyone else. ⚡️ On standby 24/7! Questions are always welcome. If you find yourself tilting your head in confusion or thinking, "What on earth does this mean?" while taking the course, don't suffer alone! Feel free to knock on my door anytime via the bulletin board or my email. Just like an Uninterruptible Power Supply (UPS) that provides power without stopping for a single second, I'll always be waiting here to support you. 🔋 I hope my fierce survival story becomes a powerful weapon in your level-up journey. Now, shall we go smash through the headache of automatic control together? Let's go! 💖

Hello, I'm Dante. Using the Codex × Hermes combo, I will personally spend 8 weeks building a one-person AI office where a single operator manages four subsidiaries (Text, Short-form, Data, and Finance) simultaneously.

AI Workflow Automation + Passive Income Generation in Just One Click!!! The ultimate tool for workflow automation, Make—were you hesitant to start because of its complex settings and features? Implement the automation of your dreams yourself with step-by-step lessons that even beginners can easily follow.

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.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

Innovation in Work Efficiency and Creativity – Agentic AI AI is now evolving beyond simple automation into a new stage where it can judge and execute on its own. Agentic AI is at the center of this change, serving as a core technology that helps businesses and organizations operate smarter. In this course, we have organized the content clearly and simply so you can understand how Agentic AI connects decision-making with execution and how it can be applied to practical work. Discover the essential knowledge for every professional preparing for the future right now.

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.

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!

Do you still code directly these days? Now is the era where even vibe coding is too much hassle! Become an AI automation expert with n8n without coding. A practical automation workflow design guide you can apply immediately at work Action-oriented automation strategies that maximize work efficiency. Build intuitive workflows with n8n and experience workplace innovation

This is a Silicon Valley internship and 30 Big Tech system design & open-source contribution mastery course that ensures job security even in the AI era. In this course, you will design and scale 30 real-world system designs using cases from Big Tech companies such as Netflix, Tesla, Meta, and Tinder. Finally, you will design your own service from architecture to DB and scaling.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is currently the technology that has come closest to AGI. In this lecture, we will explore LLM and Agent technologies from a layperson's perspective.

Learn the entire process of running the latest Gemma 4 model directly on your MacBook without the burden of paid API costs. You will master performance maximization techniques using Apple Silicon's Metal API and optimal parameter settings for different VRAM capacities, while gaining the ability to build production-level local AI infrastructure based on FastAPI.

AI writes on Slack and Notion for me? Work smarter with MCP, which goes beyond the limits of existing AI!

AI Finally Gets a Body - What is My Survival Strategy in the Age of Robots? Physical AI is not just about robots. It is an overthrow of the economic paradigm created by the grand convergence of artificial intelligence, robotics, and energy technology. This course explores various perspectives, from the background of Physical AI's emergence to the strategies of global big tech, hardware sovereignty, and future social issues such as robot taxes, basic income, and global inequality.

AI proficiency determines your salary. Leave the tedious, repetitive tasks you do every day to AI. 'Almost Everything About Work Automation' teaches you how to create an automation system that handles your work on its own with just a few clicks, even without any coding knowledge. You will be freed from boring paperwork and data organization, and transform into an 'AI-savvy professional' who stays ahead of others.

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.

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.

We are launching a "Complete Reading Challenge" for the book "AI Agent Engineering," a practical guide to building proactive agent systems using LLMs. This book covers the core components of agents step-by-step, from tool selection to orchestration (designing planning and execution flows) and memory management. It also explores how to scale into multi-agent architectures where multiple agents collaborate by dividing roles. Furthermore, it expands your perspective from "how to build" to "how to operate" by covering essential production-level topics such as reliability, security, and governance. Do you want to design agent systems that go beyond simple automation to operate safely and transparently according to human intent? Join the book and the challenge to discover a concrete roadmap for turning your ideas into production-level systems!