Artificial Intelligence Anyone Can Understand for All Office Workers

Navigate the data ocean, explore AI's future. This course explores the vast tech currents of Big Data and AI, examining their innovation in our lives and work.

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

Without a single formula or line of code, this penetrates the essence of basic statistics necessary for AI development and application.

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!

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

AI is no longer a choice, but a matter of survival. Reduce development that took 3 days to 30 minutes, delegate repetitive tasks to AI, and focus on solving more important problems. With AI-native development methods that you can apply immediately in real work, not just theory, we'll show you the definitive way to be recognized as a "developer who's good at AI" at your company.

A basic textbook and practical guide covering principles and practical uses of various generative AIs like ChatGPT and Claude, and for their effective introduction and utilization.

You can learn the concepts and methods of 'service design' needed when planning applications or web services built on LLM, a type of generative artificial intelligence (generative AI).

With ChatGPT, create your own content (songs, comics, blogs, books, papers, etc.) and create new businesses.

If you've ever thought... "I wish I had JARVIS, Iron Man's perfect assistant"?! Complete everyone's JARVIS to help with your smart work management!

"My ideas can make money? AI monetization, is it really possible?" Are you frustrated with the same monthly salary every day? Do you have brilliant ideas popping up in your head, but feel lost about how to actually get started? You hear that 'AI' is all the rage, but does it sound like a distant story for someone like you who isn't tech-savvy? What if you could turn your ideas into 'sellable digital products' without knowing a single line of code or having any marketing experience, and even create a system where money comes in while you sleep? This course is a practical guide that shows you exactly 'how' to do that.

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.

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 comprehensively learn everything from establishing AI-based system test strategies to designing and executing test cases, and even methods for utilizing AI to support testing. This course is conducted based on ISTQB's AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

Using block coding Entry, foster computational thinking and problem-solving skills, and through Entry AI tools, learn artificial intelligence concepts and practice simple machine learning.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

Depending on how you write the prompt, AI's capabilities can expand infinitely. The benefits gained from collaboration with AI depend on the clarity of the questions and the provision of prior information. Now, try to effectively utilize GPTs to perform efficient and purpose-driven tasks.

This course provides UX principles and practical know-how necessary for planning and designing AI products. We will explore user-value-centered AI utilization methods, UX planning and design methods using AI, core UX principles and theories learned through everyday design cases, and UX research and data analysis methods for providing a better experience.

Easy, powerful, always-on data analysis! Orange 3: Simple, no coding!

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

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!