A Machine Learning Story Explained in Simple Terms That Even a Die-Hard Liberal Arts Student Can Understand at Once

Understand the essence of the job market through economic and statistical concepts, and redesign employment and career-change strategies as data-driven probability problems rather than relying on intuition or "spec" (credentials) competition. Instead of simply accumulating more credentials, this strategy shifts the "playing field" to a rational recruitment market by using AI to directly collect and analyze job postings and corporate data to select markets and openings with a high probability of success. By utilizing AI as a tool built upon core economic and statistical concepts, we systematize document, interview, and application strategies, presenting a way for even non-majors to develop a data-driven employment mindset. Furthermore, we provide a method for non-majors to directly implement a 24-hour AI recruitment assistant—which can be used immediately as a portfolio piece—through AI-based "Vibe Coding."

As the only hands-on course in Korea where you can experience Generative AI, Vibe Coding, and DX/AX all at once, it provides motivation and a vision for how non-majors can utilize AI technology for development in actual business and daily life. Rather than simple theoretical explanations or introductions, it helps you expect practical career improvement by providing opportunities to complete portfolios closely related to real-world tasks and practical automation projects. It is a course that even beginners can easily follow, from tool installation to actual service creation.

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.

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

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

This is a course where you learn the basic calculus and PyTorch's Autograd functionality needed to get started with deep learning.

An introductory AI lecture tailored for those new to artificial intelligence! It easily summarizes core concepts from data to generative AI. The goal is not to build AI, but to achieve a level of practical understanding where you can ask the right questions and explain the technology.

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

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.

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 :)

A comprehensive compilation of approximately 130 ML/AI engineer interview questions and answers learned through about 20 interviews

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.

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.

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.

This is an introductory lecture designed to help you understand the core concepts of non-invasive EEG-based Brain-Machine Interfaces (BMI) and the process of Motor Imagery-based signal processing. It provides an easy-to-follow explanation of the entire workflow, from the basic principles of EEG signals to Motor Imagery experiments, data processing, and how actual BMI systems operate.