Manufacturing Innovation and AI Big Data for CEOs and Leaders

Find your way through the sea of data and explore the future of artificial intelligence. This course explores the two massive technological trends of Big Data and Artificial Intelligence (AI), examining how they are bringing innovation to our lives and work.

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.

Systematically learn the core concepts and operating principles of Imitation Learning and VLA (Vision-Language-Action) technologies, which serve as the foundation for Physical AI. Understand the mechanisms through which Physical AI interacts with the real world through actual case study analysis.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

Many people use ChatGPT and frequently hear about recommendation systems and autonomous driving, but when asked to explain “why they work the way they do,” they often struggle to find the words. If you only follow coding courses, the code runs but the underlying principles don’t stick. If you only take math courses, you see the formulas but can’t connect them to AI. Overseas courses can feel intimidating because of the English, while introductory courses in Korea tend to focus heavily on hands-on practice. While building real models in AI competitions, running Everyone’s AI, and meeting thousands of learners, I repeatedly saw the same thing: the obstacle wasn’t how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation became disconnected from one another. This course bridges that gap. From how functions define inputs and outputs, to how derivatives determine the direction of learning, and how backpropagation passes errors backward, it explains everything in one continuous flow—from the fundamentals of mathematics to neural networks and softmax. Rather than memorizing formulas or simply copying code, this course helps you understand “why” through equations and intuitive illustrations. For anyone who wants to truly understand AI, it will serve as a solid first step toward moving on to deep learning.

🔥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.

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!

The Master Guide to AI for Java Developers! This course shatters the prejudice that AI is the exclusive domain of Python and empowers your Java applications with AI-driven eyes, ears, voice, and even coding capabilities. Grow into an AI Architect based on the OpenAI API. You will directly develop various AI experts such as an AI writer, translator, artist, and coding companion. You will also build a multi-functional AI assistant that integrates core features including text summarization, sentiment analysis, image description, Java code generation, speech-to-text/text-to-speech conversion, and image/subtitle generation. This is a golden opportunity to maximize the competitiveness of existing Java systems by learning how to assign AI roles through system messages and even implement harmful content moderation.

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.

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.

In the AI era, developers must become “integrators,” not mere coders. This course covers how to combine Unreal Engine with the latest AI tools, such as ChatGPT and Claude, throughout the actual C++ development process. Through practical examples designed by YAL, an Unreal-certified instructor, you will build a development environment where AI generates, analyzes, and assists with code, and complete a next-generation C++ workflow that integrates AI with Unreal.

Did you give up while learning Verilog and EDA tools? A semiconductor professor who designs NPUs himself will show you how to understand everything from CPUs to NPUs by calculating with just 9 Excel files.

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.

Just say it, and AI will do it! From files and reports to emails. The Claude Cowork practical course for full automation without coding!

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

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 :)

Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

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.

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