[The Ultimate Manufacturing DX] AI Practical Application Strategy Master Class for 7 Major Industries

The shipbuilding and maritime industry is a representative field of traditional heavy industry, but it is securing new competitiveness through the combination of digital transformation and artificial intelligence (AI) technology. This course is structured around specific application cases actually used in the field, such as AI-based shipyard production management, ship design automation, maritime logistics optimization, and predictive maintenance system construction. With a field-centered approach, you can understand not just simple theory but **'how AI actually works in shipyards'**. Additionally, it covers data infrastructure construction, model training, and constraints specific to the marine environment that must be considered during the AI technology implementation phase.

If you've been studying information security, you've probably had this thought at least once: "Firewall, CSPM, SIEM... I've heard the names, but I'm not exactly sure what they do." This course is designed to help beginners in information security clearly grasp the concept of the 'security solution landscape.' From the concepts and key features of each solution to their pros and cons, use cases, and the latest trends... With a total of 23 lectures, why not clear up the vagueness surrounding security solutions right now?

The semiconductor industry is a representative manufacturing sector where production facilities, control systems, partners, and data are closely interconnected. For this reason, even a small security incident or system failure can lead to production shutdowns, quality issues, delivery delays, and technology leaks. This lecture explains the OT security threats that can occur at semiconductor manufacturing sites in a way that even beginners can easily understand. Rather than focusing on complex technical explanations, it centers on what security problems can arise in the actual field, why the semiconductor industry is a target for attacks, and how security incidents impact companies. Furthermore, it examines how security threats are evolving as manufacturing environments become more intelligent with the spread of AI and smart factories.

This course examines major security threats and incident cases that can occur in the process control environment of the chemical industry, providing an easy-to-understand approach to AI-based anomaly detection and response. Participants will learn practical OT security, focusing on risks unique to the chemical industry such as production shutdowns, manipulation of process values, and hazardous substance leaks.

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

[Introduction to Security Incident Response: Learning through Scenarios of Web Server Hacking, Ransomware, and Account Takeover] This course examines why various information security incidents—such as web server hacking, ransomware, account takeover, and internal penetration—occur and how they spread, categorized by incident type. Students will follow each incident scenario to understand the attack flow, analyze the core causes of the incident, and learn how to resolve issues and prevent recurrence through security architectures such as firewalls, DMZ, access control, privilege management, backup, and log monitoring. Rather than a simple explanation of security concepts, this is a scenario-based introduction to information security that provides a comprehensive understanding from root cause analysis to resolution strategies.

"Understanding Manufacturing Security in the AI Era through Field Cases" is a course designed to provide an easy understanding of OT security, which is growing in importance within smart factories and AI-based manufacturing environments, focusing on real-world field cases. This lecture explains core concepts frequently encountered in manufacturing sites, such as OT, ICS, PLC, SCADA, HMI, and MES, and compares the differences between IT security and OT security. Furthermore, it examines security threats that can occur on the manufacturing floor—such as ransomware, remote access exploitation, partner account hijacking, production line shutdowns, equipment malfunctions, and data forgery—through case studies. Through this course, students will understand the basic structure of OT security in manufacturing and identify the security perspectives necessary to protect production facilities and industrial control systems. Additionally, it provides a common foundation for further learning in industry-specific manufacturing security courses.

Beyond the era of simply using AI, we are now in an era that requires people who can design AI. This course is a master roadmap that systematically organizes the entire design map of AI agents, from "Introduction → Structure → Design → Operation." Based on RAG, Tool Calling, Memory, and Multi-Agent structures, you will learn how to design and scale AI agents from an architect's perspective. If you want to grow into someone who designs AI systems rather than just someone who "uses" AI, this course will provide the direction.

This course is a specialized program that covers practical application strategies for utilizing artificial intelligence (AI) in the construction and civil engineering fields. In the construction industry where digital transformation is accelerating, AI is driving innovation across design, construction, safety, and quality management. This course focuses on learning practical application cases that can be utilized in actual field sites, including smart construction, digital twins, AI-based equipment control, safety monitoring, and quality verification.

"Smart Factory—it sounds cool, but can't quite picture it in your head?" The news talks about the 4th Industrial Revolution and AI factories every day, but when you actually look for information, it's full of "alien languages" like PLC, MES, ERP, and Edge Computing. What non-majors need isn't a complex circuit diagram, but a 'map' that shows how the whole system works. This course strips away difficult engineering jargon and explains everything about smart factories through intuitive illustrations and analogies that anyone can understand.

"The Fourth Industrial Revolution, AI factories... The terms are familiar, but can't you visualize the structure?" Smart Factory, often called the future of manufacturing. While related articles pour out and it appears as a required competency in job postings, the barrier feels far too high for non-majors. "Did you give up because of abbreviations that sound like alien languages, such as PLC, MES, or ERP?" "Are you afraid to even start because you lack knowledge in coding or engineering?" Don't worry. This lecture deals with 'structure,' not 'engineering.' Instead of complex text, we will help you complete a full map of the smart factory in your head using intuitive drawings and diagrams, just like assembling Lego blocks.

This course covers practical strategies for leveraging AI in the machinery and metal processing industry to enhance productivity and quality. It analyzes key process data from CNC, welding, cutting, and other operations, explaining how AI technology is applied to reduce defect rates, enable predictive maintenance, and manage worker safety through real-world case studies. Learners will understand AI-based processing optimization methods that can be immediately implemented in the field, from small and medium enterprises to large corporations, and gain insights to realize manufacturing innovation in global competition.

This course covers how to leverage AI in the food and pharmaceutical manufacturing industry to enhance productivity and quality. Through real-world case studies, you'll learn how AI is applied in key stages such as raw material management, production processes, quality inspection, and regulatory compliance, while understanding defect rate reduction and efficient operational strategies. Through this course, learners will master ▲characteristics of the food and pharmaceutical industry and the necessity of AI adoption ▲data-driven quality management ▲predictive maintenance and equipment optimization ▲regulatory compliance monitoring and risk management ▲analysis of domestic and international implementation cases, gaining strategies and insights that can be immediately applied in the field.

The chemical and fine chemical manufacturing industry is a sector that requires complex processes and high safety standards. This course covers strategies for leveraging AI to increase productivity, reduce costs, and enhance safety. You can systematically learn practical skills centered on AI application cases that can be immediately applied in actual field settings, including process data analysis, quality prediction, equipment predictive maintenance, and environmental regulation compliance.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

Digital transformation (DX) is not a choice but a necessity. This course serves as the first step in digital transformation for all employees, providing easy and clear explanations from core DX concepts to business innovation cases. Don't fall behind in this changing era—develop the power to grow yourself and lead organizational innovation! Please don't overthink this lecture and take it with a light heart.

The Key to Unlocking the Value of Ideas: Intellectual Property Ability Test (IPAT) Core Lecture This course goes beyond simply passing an exam; it is a process of learning practical methods to protect invisible 'ideas,' create value, and further transform them into powerful 'assets.' We help you eliminate the vagueness surrounding Intellectual Property (IP) and grow into a competitive expert in any field. Have you been hesitating to even start studying because of the massive official textbook? This course does not cover every single detail of the official National Certified Intellectual Property Ability Test (IPAT) textbook. Instead, it is structured by selecting only the core concepts and high-scoring points that frequently appear on the exam. Exam preparation is not necessarily better just because it's long. The secret to passing is learning only what is necessary, accurately, and repeatedly. Discover that secret in this lecture.

**"Digital Forensic Techniques by Byte Detective: Evidence Collection and Securing"** is a course focused on how to collect and preserve digital evidence required in cases of cybercrime, internal information leakage, and security incidents. From the basic concepts of digital forensics to the principles of evidence collection, maintaining integrity, and legal procedures for securing evidence, this course explains everything through case studies and procedural steps so that even beginners can understand. You can systematically learn the forensic mindset and core concepts of securing evidence that are essential to know before using professional analysis tools.

Incident response is less about mastering tools and more about knowing what to judge first. Rather than focusing on how to analyze malware, this course explains the realistic thought process of determining **"whether the current situation is an actual incident and what actions to take first."** Using webtoon-based real-world scenarios, we have organized the entire flow of incident response—from identifying signs of intrusion to incident assessment, initial response, containment, and analysis/forensics—in a way that even non-experts can understand. This is not just a collection of fragmented technical explanations; it is a course where you learn the decision-making criteria and response perspectives that practitioners can apply immediately in the field.

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

A first in Korea! This course teaches you how to automate Word documents for business using ChatGPT. You can easily create various questionnaires, business reports, meeting minutes, and contracts, and apply them directly to your work through hands-on practice.

A comprehensive compilation of approximately 130 ML/AI engineer interview questions and answers learned through about 20 interviews

The lecture explains why the harness is more important than the model through the Claude Code architecture. It covers how to design the Claude Code task system using six structures: Context Harness, Permission Harness, Verification Harness, Tool Harness, Subagent & Worktree Harness, and Debugging Harness. Afterward, it connects these concepts to CLAUDE.md, session management, large codebase control, worktree parallel operations, error log analysis, and tracking environment variables and configuration scope issues, establishing the standards for continuously using Claude Code in practical backend and DevOps workflows.

Learn how to understand advanced machine learning models without math and implement them easily in Python.

The AWS Certified AI Practitioner practice exam course is a core summary program designed to help you pass the certification. First, you will quickly review the basic concepts of AI/ML and Generative AI, and learn the features and use cases of key AWS AI services that frequently appear on the exam (e.g., SageMaker, Rekognition, Polly, etc.). Based on this, you will practice determining which AI solutions are suitable for various real-world scenarios, while also covering topics related to the responsible use of AI models and data security. Finally, you will learn problem-solving strategies for each type of exam question and prepare for the actual test through mock exams. This course allows you to prepare for the exam efficiently in a short period of time.

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.

This is an introductory course on a mobile AI assistant workflow with vision capabilities: when you send a photo of a receipt via Telegram on your smartphone, OpenClaw and Gemini 2.5 Flash automatically analyze it and organize the data into your PC workspace.

Have you introduced AI but still find yourself losing time to repetitive tasks? The problem isn't the AI; it's the 'system.' This course guides you through the process of building a team of six AI assistants—including an executive assistant, content assistant, marketing assistant, CRM assistant, and personal productivity assistant—all using Notion AI. Based on my experience automating hundreds of repetitive tasks in real-world business settings, we will build actual workflows from start to finish, covering everything from organizing notes and automatically generating meeting minutes to blog writing, CRM construction, and automated KakaoTalk notifications. After completing this course, you will have a system where your own AI assistant team works automatically, powered solely by a Notion Business plan.

An introductory AI lecture tailored for those new to artificial intelligence! It easily summarizes core concepts from data to generative AI. The goal is not to build AI, but to achieve a level of practical understanding where you can ask the right questions and explain the technology.

Evolve Obsidian from a simple notepad into a personal knowledge database and build an AI assistant that understands your notes and documents using Claude Code. Create custom workflows to automate repetitive tasks and complete a knowledge system where daily work and learning compound and transform into your own unique expertise.

This special lecture concurrently prepares for Microsoft AI-900 certification, featuring the latest content reflecting May 2025 exam scope, combining theory and practice, including AI overview, machine learning basics, computer vision, natural language processing, document intelligence, knowledge mining, and generative AI.

No coding knowledge required, build your very first service with Google Stitch An all-in-one AI development workflow that connects everything from planning to deployment in a single stream

This is a practical course that uses Codex to connect business planning, website creation, marketing materials, task organization, and email automation. Even if you are not a developer, you will learn how to delegate tasks to AI and refine the results using Notion, Linear, Figma, and Google Workspace in an actual workflow sequence.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

You can benchmark manufacturing innovation methods for semiconductors and run machine learning and deep learning with semiconductor data without coding.

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

In the AX era where AI technology and UX design converge, what strategies should product teams use to design customer experiences? A 17-year veteran senior researcher from a major corporation and knowledge sharer with a PhD in HCI will share insights based on the latest trends, use cases, and research findings.