Understanding the Training Process of Supervised Learning in Artificial Intelligence, Regression, and Classification

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.

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.

The most rapidly emerging task automation tool recently is 'Claude Code.' Through Claude Code, you can deploy AI agents within your computer that work exclusively for you and create a system where repetitive tasks are assigned to the AI one by one. Try automating 100% of the once-exhausting tasks like PPTs, reports, and Excel work.

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 :)

Are you using AI but still unable to get it to do the work for you? This course is not just about simple usage; it is a process of creating your own "high-performing AI assistant." By mastering everything from prompt principles to personalization settings and task automation at once, you can delegate repetitive tasks to AI and create a new way of working that allows you to focus on your core responsibilities.

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.

I will teach you Vibe Coding systematically from the ground up, following the exact curriculum refined while teaching over 10,000 students by a former Tech Lead with 10 years of experience in New York and LA startups. Together, we will build my own service, 'Kagong Map,' from scratch, learning the entire real-world workflow—from research, development, and deployment to operation and automation by delegating tasks to AI agents. I promise you two things: first, you will grasp the big picture of how a service operates so you can judge the AI's output yourself; second, you will learn how to embed workflows and standards into the environment so the AI produces consistent results every time. When you make the judgments and the AI follows orders effectively, your ideas become real services.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

A New Framework for Human-AI Collaboration to Amplify Human Thinking How should AI be utilized to maximize the amplification of human capabilities? On this new stage of creativity, what criteria should govern human-AI collaboration? Furthermore, how should jobs and workflows be designed? This course presents a new framework and execution strategies to answer these questions.

In this class, you will learn the core features of Adobe Firefly so that even design tool beginners can use them, and develop the ability to quickly create key marketing content such as banners, card news, and product detail pages using AI.

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 basics of using SearchGPT, OpenAI’s AI-powered search tool, as well as its various features and applications. You will learn how to use and apply its text-based functions through prompt input.

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

Stop getting student-record planning. I’ll reveal the entire consulting playbook used by a college admissions consultant with 30 years of experience.

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

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.

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.

You have likely seen many misleading lectures about monetizing AI music. Yes, AI music monetization is possible. It is simply not difficult enough to justify paying expensive tuition, nor is it a way to make easy money. This is an action-oriented course for those who are interested in AI music monetization but feel lost on where to start. Based on my personal experience of creating AI music, organizing albums, distributing tracks, and utilizing YouTube and TikTok, I have structured the workflow—from music generation and album planning to file preparation, distribution, and monetization setup—so you can follow along from the very beginning. This course does not guarantee profits; rather, it is designed to help you experience the entire process of releasing AI music and preparing for monetization from start to finish.

Learn the core principles and practical implementation of Classic RAG, Graph RAG, and Agentic RAG. Design a dynamic routing system that optimizes token efficiency and latency, and build a long-term memory system that combines GraphRAG’s relational network reasoning with Agentic RAG’s self-evaluation loop. This course develops advanced RAG architecture design skills that can be applied directly in real-world settings.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made 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 and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."