AI Fundamentals: Learning the Principles of Machine Learning and Generative AI Through Illustrations

Understand the operating principles of generative AI models using deep learning and acquire practical application skills through hands-on exercises.

Do you want to take your first step into the world of data? Learn machine learning and deep learning, the core technologies of AI, with Python. This course will guide you step by step, from the basics of machine learning and deep learning to practical applications. Traditional machine learning and deep learning are based on many of the same principles and technical systems. Therefore, this course does not separate the two into separate subjects, but rather organizes them into one connected course so that beginners can increase their understanding of machine learning as a whole.

The era when AI simply answered questions is over. Now is the age of LLM Agents that make decisions and take action on their own. This beginner-friendly course lets you learn the core principles and structure of agents by implementing them firsthand in just 90 minutes of hands-on practice. With complex theory kept to a minimum and a code-focused approach, you’ll directly experience how AI makes decisions and uses tools. Go beyond prompt engineering and take your first step toward AI automation with us.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

An introductory course that teaches the core principles of AI—from Python and data analysis to machine learning, text mining, deep learning, and Transformers—with a focus on theory and hands-on practice of key topics.

Understanding AI Principles and Structure: From How AI Works to Decision-Making and Learning How does AI work, what does it base its decisions on, and how does it learn and improve? To truly understand artificial intelligence, it is important to go beyond simply using AI tools and understand the core principles and structures that make them work. This course provides a systematic explanation of AI’s operating principles, data processing and decision-making, and the mechanisms behind learning and change as part of a unified process. Rather than focusing on complex mathematical formulas or difficult programming, the course uses intuitive explanations and a variety of examples to help beginners easily understand AI’s internal structure and how it works. Through this course, you can gain a broad understanding of the process AI uses to produce results, the basis for its decisions, and how learning improves its performance. Go beyond simply using AI and build the foundational skills to understand how it works and its limitations, and to use it more effectively in your work.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

The AI I trained myself determines stock allocations and places orders on its own. Starting with Python basics and the principles of reinforcement learning, you’ll train your own trading AI using real stock price data. Test it against the market, compare performance, and improve your strategy by adjusting reward functions and training settings. Connect your trained model to Korea Investment & Securities paper trading and automated execution on AWS to build your own automated trading AI—one you can continue to modify and develop even after the course.

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.

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.

- Practical Kaggle Competition Project: From Data Exploration to Submission - Beyond Tutorials: Follow a Real Kaggle Competition from Start to Finish Have you signed up for Kaggle and completed the Titanic tutorial, only to feel lost about where to start when facing a real competition? If you simply copy someone else’s notebook and submit it, you’ll eventually hit the wall of wondering, "Why isn’t my score improving?" In this course, we’ll work through an actual Kaggle competition from start to finish, covering data exploration, validation design, feature engineering, modeling, and submission. Instead of memorizing code, you’ll develop an intuitive understanding of the competition mindset: “What should I try and why, and how can I validate its effectiveness?” [One line about your competition participation, awards, or practical experience]

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

We provide guidance on what to study for those starting AI development.

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.

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.

This is a linear algebra course that explores the meaning of vectors and matrices through illustrations and works through calculations using small examples. It covers the mathematics used in linear regression, PCA, SVD, tensors, and attention step by step. No programming knowledge is required.

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.

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.