Generative AI Utilization Methods for Businessmen

This course is designed to simultaneously develop both fundamental theory and practical application skills needed to proficiently execute AI modeling projects in real-world settings. Learners will understand the overall flow of AI projects and experience step-by-step processes through hands-on practice: problem definition → model design → data acquisition and preprocessing → feature extraction → model training and validation. Additionally, by learning how to select and manage optimal AI models in various situations, learners can build practical AI capabilities that can be applied to actual business environments. Through this course, learners can systematically understand the entire AI modeling project process and strengthen their data-driven problem-solving abilities based on this foundation.

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.

As the digital transformation of the manufacturing industry accelerates, production facilities, control systems, and industrial networks are becoming increasingly connected to external systems. Along with this, the possibility of cyberattacks leading to production shutdowns, quality degradation, technology leaks, and safety accidents is also growing. The [OT (Manufacturing) Security] Master Class for 4 Major Manufacturing Industries is a course that examines OT security threats that can occur in the automotive, semiconductor, chemical, and food/pharmaceutical industries, focusing on real-world manufacturing site cases. Participants will understand how production processes and facility environments differ across each industry and systematically learn about key security threats, causes of accidents, and response strategies specific to each sector. The course covers a wide range of topics in a single program, including smart factory and supply chain security in the automotive industry, protection of core processes and technical information in the semiconductor industry, control systems and safety accident response in the chemical industry, and quality, hygiene, and GMP data protection in the food and pharmaceutical industries. Rather than simply listing complex technologies or attack techniques, the course explains "what problems can occur," "why they occur," and "how to respond on-site" in a way that is easy for even beginners to understand. Through this lecture, you can compare the characteristics of the four major manufacturing industries and the differences in OT security, and learn the basic direction of security inspections and accident response required at manufacturing sites.

1. Mock exam problem-solving for passing AICE Associate, Korea's only state-certified AI certification. 2. A course featuring 12 mock exam sessions with the same question types as the actual AICE Associate exam.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

With the advancement of generative AI, many companies are pursuing AX (AI Transformation). However, in the actual field, there are not many educational programs that systematically teach what to do and how to do it compared to the demand that "AX must be done." While most AI education focuses on how to use generative AI or write prompts, this course focuses on the "AX Strategy Planning Methodology" for transforming corporate tasks and services into AI-based ones. This course is based on the practical experience of a 25-year expert who has performed DX/AX consulting and training for large corporations, the financial sector, public institutions, and various companies in the actual IT field. It allows you to learn from a practical perspective not just how to use AI well, but which tasks should undergo AX, to what extent AI should make decisions, how to control risks, and how to design and verify AX projects. Furthermore, it systematically covers core content necessary for actual AX implementation, from understanding the essence of AX to AI thinking systems, AX design methodology, Human-AI role separation, Guardrail design, and KPI and verification structures. In particular, it goes beyond simple theoretical education. Based on real corporate cases such as insurance underwriting, customer consultation, manufacturing quality, internal work support, marketing recommendations, development productivity, and management decision-making, it provides guide templates and sample materials for AX problem definitions, AX hypothesis definitions, AX performance designs, AX service structure diagrams, Guardrail designs, and AX verification plans. This ensures that after taking the course, you can secure the practical capabilities to easily plan and promote actual AX tasks. The biggest differentiator of this lecture is that it teaches how to plan and design AX from the perspective of someone who actually performs AX projects. Specifically, by providing AX strategy planning output templates and seven industry-specific AX cases that can be used in the field, it is designed so that students can refer to cases most similar to their own work and apply them immediately by modifying them to fit their environment. In short, this is not a course to create AI users, but a process to create AX strategy planners who can design and execute a company's AX.

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

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 :)

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

Customer analysis completed with Excel and Orange, from cross-selling to A/B testing and churn risk management all at once

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

How long will you keep struggling with repetitive daily research and document writing? I, too, was an ordinary office worker who worked overtime every day trying to find key insights amidst an overflow of information. However, after integrating Gemini 3 into my workflow, I experienced a significant increase in work speed. Boost your productivity by utilizing Gemini in your tasks.

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.

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will gain a broad range of knowledge, from establishing AI-based system testing strategies to designing and executing test cases, as well as utilizing AI for test support. This course is based on the ISTQB AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

You can learn about Text-to-SQL systems that generate and query SQL statements in real time in response to user questions.

"Throw away the cliché AI theories and chatbot examples scattered all over YouTube. This is Korea's one and only '7 Major Industry Integrated Manufacturing AI Bible' that understands the language of manufacturing and pierces through the essence of the process." Hello, I am Byte Detective, your knowledge sharer. Currently, countless companies and practitioners are calling for 'Manufacturing DX (Digital Transformation)' and 'AI Adoption.' However, the majority of courses on the market are limited to vague theories from IT developers or consultants who have no understanding of the harsh practical realities of the manufacturing field. This course is not a lecture for the sake of theory. By organically combining the actual domain knowledge of Korea's top 7 representative manufacturing industries with the latest AI frameworks, it provides a 'Practical Manufacturing DX Scenario Architecture' that can be applied to the field starting tomorrow.

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.

The purpose is to improve, compared with last year, awareness campaigns that are not defined in the regulations and often end merely with the distribution of promotional gifts, by referring to best practices from various organizations, given the current situation in which the performance of events and campaigns such as Information Security Day or Personal Information Protection Day, held monthly or annually, is gradually declining. These events must now move beyond gift-distribution activities organized by the Information Security Team and become events in which all employees participate and uphold security together. It is necessary to build mutual understanding through the establishment of a communication system for embedding security into the organization. Ultimately, employees must have a good understanding of information security and the Personal Information Protection Act so that the organization can realistically protect itself from hacking and insider threats. Since the departments other than the Security Team—including those responsible for commissioning various projects, managing entrusted service providers, controlling applications and security settings, planning various informatization projects, and conducting IT audits—are designated to take overall responsibility, the Security Team is distributing security-related tasks among the respective departments. However, the current situation is that each department is merely expressing dissatisfaction. This opportunity should therefore be used to move away from that situation, as internal consultative bodies alone are insufficient. Security-related scoring criteria must be reflected in the powerful personnel evaluation system. Through unannounced audits, those who fail to comply should be disciplined through pay reductions or suspension, while those who properly observe security requirements and demonstrate achievements in support of security should receive substantial financial rewards through an incentive system. Security must be embedded in the organization so that employees take the initiative themselves. Accordingly, department and individual evaluation KPIs should be adjusted through a report to the CEO under the direction of the CISO. In addition, through a system for designating department-level information security officers and personnel in charge, the organization should strengthen its human security framework against hacking and information leakage—including the leakage of documents managed by each department and the management of service providers—by providing position allowances for these responsibilities.