Artificial Intelligence (AI) - Learning Ideogram AI that Creates New Images

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

This course is an AI music generation introductory program designed so that even learners who are new to artificial intelligence (AI) technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and proceed step-by-step through hands-on practice to create your own music using these tools. Going beyond simply pressing buttons to make music, this course guides you to understand the principles of AI models and the key factors that influence music generation, and to complete creative results by adjusting the desired atmosphere, genre, and instrumental composition. Upon completing the course, you will gain confidence in using AI in a friendly manner, along with AI music generation capabilities that can be applied to various fields such as daily life, projects, and content creation.

This is a comprehensive deep learning project course that teaches the latest deep learning techniques, GAN, BERT, RNN, and CNN, while creating various useful projects based on Python and TensorFlow 2 along with theories.

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.

For learners unsure of what to create, we provide guidance from planning through short-form completion based on hands-on production experience.

This course is designed to learn the fundamental theories and practical processes necessary for applying AI modeling and service planning to real-world work environments. Learners can acquire essential foundational knowledge to skillfully perform AI modeling projects and experience various practical procedures used in actual business settings. The course is specifically structured to provide step-by-step learning of the entire workflow from AI service environment analysis → goal setting → requirements analysis → service model design → scenario planning → utilization planning → execution plan development → performance evaluation planning. Through this approach, learners can develop systematic thinking and practical application skills for how to introduce and utilize AI from business and service planning perspectives, rather than limiting themselves to purely technical learning. Upon completing this course, learners will understand the overall planning and execution process of AI projects and will be able to strengthen their capabilities as AI service planners, PMs, and data-driven strategists in actual corporate environments.

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.

For those who have tried running hand-recognition examples but feel lost when it comes to teaching an AI your own hand gestures and turning them into something useful, I’ll guide you all the way from installation to data collection, training, and completing the web interface using just a webcam, drawing on my experience overseeing development and training vocational instructors.

Finally, I feel that the AI security review (NIS/FSI) checklists, which have been vaguely positioned over the past 7–8 years, are now accurate from a practitioner's perspective, and I have established a clear framework for how to change, establish, and operate the management system. I will explain what AI security is and how it should be improved after June 2026, based on the standards of South Korea's top-tier companies (those with the highest level of information security). (This is not a basic or conceptual explanation*) I have developed this training divided into PART 1 (Basics & Overview) and PART 2 (Advanced & Security Review), so please refer to it.

[Python for AI] This is the fourth lecture in the series. It is a lecture that covers Python grammar along with practical exercises used in machine learning and deep learning.

Generative AI for Designers🧙‍♂️ We reveal practical know-how to maximize design work efficiency with generative AI like Canva and Midjourney.

What if you could do difficult PR planning and design courses all at once? Even complete novices can finish content like a pro with AI!

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

If you want to be recognized as a machine learning expert in large-scale data environments—from understanding the core framework of Spark Machine Learning to SQL-based data processing through high-difficulty practical problems, and the ability to implement optimized machine learning models through business domain analysis—join this course.

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.

This is the fifth lecture in the [인공지능을 위한 파이썬] series. It is a course that provides practical exercises used in Machine Learning and Deep Learning, along with Python grammar.

We'll show you specifically how to work with AI at actual advertising agencies, including strategy development, reference collection, prompts, and practical implementation methods.

Can you make PPTs but still worry about time and quality? Boost both your speed and perfection with AI, from planning to design!

Putting a neural network on an FPGA — it sounds familiar, but few people have actually done it. If you want to get ahead in capstone projects, competitions, or graduation projects, you need to go beyond training a model: you must be able to build and prove for yourself how its computations actually run inside a chip. This course helps you bridge that gap between theory and physical hardware. From reading the specifications to building a Python golden model, Machine Learning training, Verilog RTL design, and FPGA verification, you will complete a neural network accelerator (NPU) for recognizing MNIST digits written in real people’s handwriting from start to finish. From a Conv layer built with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax — you will integrate nine blocks into a single chip and prove for yourself that "the Python reference answer and the hardware output do not differ by even one bit (Bit-True)." The course also covers how to overcome that frustrating moment when the simulation runs perfectly, but the results are subtly different. We will walk through practical know-how that general deep learning textbooks do not cover, one step at a time — including integer quantization, array-axis ordering conversions, and preventing accumulator overflow. Drawing on the trial and error I have experienced through extensive work with RISC-V CPU design and FPGA Bit-True verification, I have built those lessons into this course so that you do not have to repeat them. By the end of the course, you will have a complete hardware project in hand and be able to say, "I designed it myself and proved it.&quot