[5-Minute AI] A Complete Overview of AI Technologies You’ll Encounter in the Workplace Anyway

👉 A starting point for those who want to properly understand AI for the first time. If you understand what AI does but aren’t quite sure why it works the way it does, this is the course to start with. In this course, we’ll explain step by step, from the beginning, how artificial intelligence differs from traditional programs and how learning and decision-making take place. Without complicated equations or coding, the course is designed to help you understand the big picture of how AI works.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

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.

Mastering difficult AI with just one single analogy?! A first step into AI principles, understanding through core concepts rather than just terminology.

1. A practice-oriented lecture designed to help you pass the AICE Associate, Korea's only nationally recognized AI certification. 2. Learn to the level of performing data analysis and AI modeling based on Python.

The real story of an AI-native company run by GolbinHacker, the 1st place winner of Kakao AI TOP 100 and author of the bestseller "The Best Prompt Engineering Lecture." It explores how just two developers build and operate a service, along with the new competencies developers and organizations need in the AI era through real-world cases.

The limitations of LLMs? I’ll share practical solutions to real-world problems based on RAG trial and error and operational know-how from working in Silicon Valley.

You might be able to create flashy images with AI tools, but what kind of content should you actually plan? And can you create a cohesive shorts video? We'll support you through six lectures to help you tackle these aspects as much as possible.

Even when using AI, the reason you’re still busy lies not in the tools, but in how the work is arranged. By asking four questions—roles, flow, handoffs, and exceptions—you can turn your work into a single blueprint, separate what can be handled by rules from what requires judgment, and reduce how often you need to call on AI in the first place. Use an Excel file containing 1,000 transactions to lock the standards into the tool and generate reports, break tasks down with sub-agents, and hand off what can’t be done to another AI, receiving only the results and carrying on. Finally, connect a messenger as the entry point and add four safety measures, completing the conductor’s role of calling on AI only where needed rather than handing everything over across eight installments, including the OT.

This is an introductory course where you learn the core concepts of Claude Code through a 3D office analogy. From hiring a single robot in an empty office, giving it a brain and a heart, equipping it with tools, permissions, and memory, to eventually having multiple robots work together as a team—each concept unfolds as an office scene. You will master every concept by interacting with the digital course materials and complete your learning through hands-on practice on your own computer.

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.

I would like to introduce the history of AI and the perspective from which we should view the future of AI.

Learn how to structure diverse data—including students’ grades, school records, and areas of interest—using the AI-SPARS tool, and leverage an LLM to design everything from major exploration and college/admissions strategies to personalized roadmaps. Rather than simple Q&A, you can immediately apply in practice a systematic AX-style career and college counseling process that progresses from Profile-Interview-Major-College-Admissions Track-Gap-Roadmap-Smart Report-Portfolio.

The era of AI simply making PPTs "for" you is already over. What matters now is not "how well you use AI," but "how you think and design structures alongside AI." This is not a lecture that simply lists Claude and Gemini features. Through a practical PPT production workflow that you can use immediately tomorrow, we help you drastically reduce report writing time and focus on the more important task of "thinking." With a practice-oriented curriculum that can be applied directly to weekly report PPTs, go beyond shortening production time and complete your own AI-based work system. Point 1. AI Collaboration Structure Design: Speed changes when you divide roles - Division of labor: Claude for analysis, reasoning, and drafting; Gemini for visualization and slide generation - Designing a structure to use AI tools as "work partners" rather than simple utilities - Shifting work methods to reduce repetitive tasks and focus on thinking and judgment Point 2. Drastically shorten PPT production time with the R-S-D-P pipeline - Internalizing the 4-step production process: Research → Structure → Draft → Polish - Increasing work speed by standardizing the flow of report and proposal creation - Learning practical PPT production methods that reduce time by more than 70% compared to traditional methods Point 3. Structuring repetitive tasks by building your own AI automation system - Building a personalized AI environment using Claude Projects and Gemini Gems - Designing "your own AI assistant" trained on your company's tone, manner, and templates - Completing a sustainable work system by automating weekly and monthly reports

🔥Don’t have time to complete the 32-hour all-in-one course? We’ve condensed the four subjects of the Big Data Analysis Engineer written exam into around 4 hours. Connect concepts directly to actual exam questions through modified past exam questions, and receive 150 pages of lecture materials plus 13 pages of final review notes for the exam room.

See exam types and trends at a glance! Strengthen your practical skills with approximately 100 questions. The perfect preparation for the AWS AI Practitioner exam, allowing for systematic study even when preparing on your own.

"We need to do DX/AX." It's always serious in the meeting room, but when you actually get down to the field, don't you feel lost about what to do first and how to do it? You've done a few PoCs, but they never transition to regular operations You bought solutions and equipment, but they're treated as cumbersome tools on-site There's a lot of talk about data and systems, but you can't figure out how to apply them to your current line or process There's no dedicated TFT or team, so you're a practitioner who has to handle both your main job and DX/AX... This course was created specifically for people like you.

"AI will not replace people. People who master AI will replace those who rely on it." In the era of AI, where creating results has become easier than ever, human thinking and problem-solving skills are becoming even more critical. Jae-sung Kim, a former McKinsey consultant, will guide you step-by-step through advanced problem-solving skills using AI, powerful research strategies, and persuasion techniques to turn your results into reality.

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

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.