Complete AI Mastery for Non-Majors: From Machine Learning to Generative AI

This is a hands-on career and college admission project course where high school students use the AI-SPARS system to analyze their academic records, mock exam scores, and student record activities to explore their desired majors and universities. Students will compare various admission types, such as early and regular admissions, and personally design grade-specific roadmaps and execution strategies to reach their target universities. It is a practical course where high school students utilize AI-SPARS to analyze their grades and student record information while exploring desired majors, universities, departments, and college admission tracks. Participants will directly install and use the AI-SPARS program to create their own college admission profile and complete a strategic admission plan and roadmap to reach their target universities.

Middle school students will use the AI-SPARS tool to systematically analyze their interests and strengths, and explore customized careers, majors, and elective courses. The program is practice-oriented, ranging from AI in-depth interviews to career roadmap design, ultimately resulting in the completion of a personalized career portfolio.

This is a practical guide to AI-based career exploration for children in grades 4–6. Using the AI-SPARS algorithm, it precisely analyzes a child's interests, aptitudes, and values, teaching how to complete a customized career and academic portfolio prepared systematically from elementary school. Parents and teachers can directly implement a data-driven career design process that suggests a direction rather than a single fixed answer.

Design and create your own student-customized AI Tutors and AI Avatars using generative AI such as ChatGPT and Gemini along with Google Colab. From prompt engineering and data analysis to building an AI Career Exploration Platform for career and academic counseling, you will complete practical projects that can be immediately applied in the classroom.

Build a fully functional smart farm platform that integrates sensor data collection, MQTT communication, web dashboards, and AI-driven environmental analysis. Experience the entire process of agricultural digital transformation (DX) through a hands-on project, covering everything from IoT sensor integration and FastAPI/Spring-based API servers to React/Vue dashboard development and cloud deployment.

This is an advanced course where you will directly implement and interpret practical statistical techniques such as sampling distributions, hypothesis testing, regression analysis, and logistic regression using R. By utilizing AI tutors like ChatGPT and Gemini, you will learn everything from code generation to interpreting analysis results and writing reports, allowing you to take your data analysis capabilities to the next level.

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

This course is structured to provide systematic learning, from basic database concepts to practical SQL applications, based on the Oracle Database 11g environment. Through step-by-step hands-on practice, you will master core content essential for database practice, including SELECT statements for data retrieval, DML for data manipulation, DDL for data definition, and user privilege management. Furthermore, this course goes beyond simple video lectures by providing an LLM-based AI Tutor, designed to allow learners to study by asking questions and receiving answers on their own. By utilizing the AI Tutor, the following types of learning are possible: - SQL syntax explanations and example generation - Analysis and correction of SQL error causes - Automatic generation of practice problems - Q&A on database concepts - Customized review based on learning progress - Generation of expected questions for certifications and job interviews Therefore, this course is suitable not only for beginners new to databases but also for professionals and job seekers looking to strengthen their practical SQL skills.

Practical Hadoop & Hive Big Data with LLM Hadoop Ecosystem with AI Tutor This course focuses on understanding the Hadoop Ecosystem, a core technology of the Big Data era, and provides hands-on learning on how to process large-scale data using Distributed Storage (HDFS), Distributed Processing (MapReduce), and Data Warehousing (Hive). The curriculum is designed to help you master the core technologies of big data processing step-by-step, from Hadoop's basic structure and operating principles to HDFS, YARN, MapReduce, and Hive. In particular, you will build practical skills applicable in the field by performing tasks ranging from setting up a virtual machine-based practice environment to actual data processing and analysis. Furthermore, this course goes beyond simple video lectures by providing a self-directed learning environment utilizing an LLM-based AI Tutor. Students can maximize their learning efficiency through the AI Tutor, which provides Q&A for Hadoop and Hive concepts, error troubleshooting, practice problem generation, HiveQL writing support, and project learning guides.

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.

The era of Physical AI, as mentioned by Jensen Huang in 2025, is coming—alongside Perception AI, Generative AI, and Agentic AI. Experience Physical AI firsthand, quickly and easily. Explore Physical AI using Hugging Face's LeRobot library and actual physical robots.

This course is a hands-on program that covers everything from the basics of understanding and utilizing AI platforms to AI image generation technology. Learners can master the fundamental concepts and tools of AI and develop the ability to generate AI images using these skills. The course is designed to be easily accessible even for learners who are new to AI technology, providing an opportunity to quickly master AI image generation techniques that can be applied in real life.

From building analysis models to application with Orange! Learn easily and quickly! You can master the core technologies of data science without coding!

Easy and powerful data analysis anytime, anywhere! Simple with Orange 3, even if you don't know how to code!

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

Understand Transformer-based LLM architectures and GPU utilization strategies, and gain hands-on experience with the actual serving process using vLLM. This course covers the entire practical workflow, from building AI system pipelines to monitoring and multi-GPU utilization, and is designed for intuitive understanding through diagrams and practice without complex formulas.

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.

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

We will look at how to collect and analyze user requirements for planning an LLM service.

Building a Full-Stack System Without Coding: Complete the entire pipeline from data collection to AI/ML analysis and web dashboards using only Korean prompts (Vibe Coding). Owning a 100% Automated Pipeline: Secure an independent stock analysis tool that sends S&P 500 screening and investment opinions via Telegram every morning. Acquiring a Practical Data-Based Investment Framework: Master institutional-level data analysis and risk management capabilities based on source code currently in actual operation.

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.

If you're wondering, "I did the user research... but is this result truly representative of all users?" then pay attention! We will show you exactly how to add persuasiveness to your designs based on data. ✅ We explain complex statistical terms in very simple language. ✅ We carefully introduce the 3 core hypothesis tests essential for UX practice, covering everything from principles to hands-on practice. ✅ We show you how to easily analyze user data using AI—without costs or hallucinations.

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.

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

"Throw away the cliché AI theories and chatbot examples scattered all over YouTube. This is Korea's one and only '7 Major Industry Integrated Manufacturing AI Bible' that understands the language of manufacturing and pierces through the essence of the process." Hello, I am Byte Detective, your knowledge sharer. Currently, countless companies and practitioners are calling for 'Manufacturing DX (Digital Transformation)' and 'AI Adoption.' However, the majority of courses on the market are limited to vague theories from IT developers or consultants who have no understanding of the harsh practical realities of the manufacturing field. This course is not a lecture for the sake of theory. By organically combining the actual domain knowledge of Korea's top 7 representative manufacturing industries with the latest AI frameworks, it provides a 'Practical Manufacturing DX Scenario Architecture' that can be applied to the field starting tomorrow.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

When you study machine learning, you learn how to improve accuracy. But the moment you try to deploy that model onto an actual chip, you are left with questions no one ever taught you to ask: How many multiplications does this model require? What does it need to remember, and how much? Can the target chip specifications handle these demands? Machine learning textbooks focus on accuracy, while semiconductor textbooks focus on circuits, yet resources bridging the two are scarce. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs—facing situations where the model with the highest accuracy simply wouldn't fit on the board. In this course, you will build six algorithms from scratch in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy for each. You will then translate these metrics into actual FPGA board resources. While knowing the number of multiplications alone doesn't give you a sense of scale, expressing it as a percentage of the board's resources makes it easy to assess. After completing nine labs and compiling the six models into a single table, an unexpected picture emerges. We spent nine sessions counting multiplications, only to find that multiplications weren't the actual bottleneck. From there, we move on to quantizing floating-point numbers to integers, managing bit widths, and even establishing criteria for grading circuits. An FPGA board is not required. The course can be completed using Python alone, with the board serving merely as a reference for translating the measured metrics.