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.

This lecture is designed to give you a general understanding of AI without overwhelming you with difficult topics like mathematical formulas. It's perfect for learning with a light and easy mindset.

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.

Numpy and Pandas are the foundation of data analysis and the backbone of AI frameworks, and I've packed this course with the core practical know-how I've accumulated through my work and teaching experience. I've designed it with a hands-on approach so that marketers, planners, and beginner analysts can easily follow along with data cleaning and processing—the areas where they most often get stuck. Through this course, anyone can develop the skills to handle data and produce analysis results on their own, without needing help from developers.

Is the practical exam daunting because you’re new to Python? An instructor with over 10 years of experience teaching data analysis will guide you step by step, starting with a single variable and progressing through Type 1 preprocessing, Type 2 modeling and result.csv submission, and Type 3 statistical testing. Each video comes with a fill-in-the-blank notebook, allowing you to follow along hands-on in a practice environment that most closely resembles the actual test site. This makes it learning that stays with you, rather than studying that ends when you finish watching. Use the self-assessment to choose your track (complete beginner: 6 weeks · experienced learner: 3 weeks · two weeks before the exam) and take only the lessons you need.

This course is for those who have learned the basic grammar of Python and will learn the core skills used in text data analysis. I hope that those who are interested in text analysis will gain more ideas through this course and use it for a long time.

Part I Practiced in code the process of loading a PDF as page-by-page Documents, chunking it, converting the chunks into vectors through embeddings, and indexing them in a vector store. Learned the structure of converting questions into vectors, performing similarity searches (including scores, vectors, and MMR), wrapping the process as a Retriever (Runnable), and connecting it to RAG. Established practical standards for application by organizing search quality checks, security and governance (including prompt injection), and common mistakes. Part II Implemented in R the random forest algorithm, which overcomes the limitations of decision trees, and learned how to accurately interpret model results. Built the ability to read and explain key metrics such as OOB error, variable importance, and confusion matrices using iris data and real-world biomarker data. Completed an end-to-end workflow applicable directly in practice, covering hyperparameter tuning, cross-validation, and ROC curve analysis.

This hands-on course teaches you how to use AI to analyze sales and inventory data and create evidence-based ordering plans. You’ll learn how to clearly communicate your objectives, constraints, and desired outputs to AI, then apply those skills to sales analysis, demand forecasting, and multi-item order optimization. Using the provided training data, prompts, and code, you’ll verify calculation results and test the basis for your decisions by changing the conditions. By the end, you’ll complete an ordering plan for 30 products and a one-page decision-making report, which you can use for job preparation and to expand your practical skills.

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.

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

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.

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.

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

On your way to work tomorrow morning, your phone will have “Today’s stocks selected by the system and the reasons why” waiting for you. An AI-powered Korean stock auto-trading system that reads foreign and institutional buying trends and handles everything from purchasing to taking profits and cutting losses automatically—you build it yourself, making it truly yours, rather than relying on a program made by someone else.

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.

Master the Big Data Analysis Certification—an essential credential that is by no means easy to self-study—in a short period using R, an easy-to-learn data analysis tool!

Revolutionize your stock investing with AI! In this course, you’ll build a fully functional AI automated trading system that targets the top 25 Nasdaq stocks. Gain experience and know-how through paper trading with the Korea Investment & Securities API, and take advantage of this special opportunity to conduct AI-powered automated stock trading with a real account. Even if you’re a complete beginner, there’s no problem. We’ll explain the basic concepts of stocks and why you should invest in U.S. stocks in an easy-to-understand way, and provide all source code along with remote support. From now on, investing is essential for the next 60 years of our lives. Build your own AI investment partner to accompany you!

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

We solve the challenge of knowing Python syntax but struggling to build services through hands-on experience at Naver and step-by-step practice.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.