Mastering Semiconductor Evaluation and Analysis Roles - Application Strategies for Samsung/SK Hynix PE/AE

“I have the data, but I don’t know how to look at it.” This is the most common concern for those starting on-chain analysis for the first time. In this course, a 10-year veteran data analyst and on-chain researcher fully reveals all the tools and interpretation techniques used in actual market analysis. ㆍWhich indicators react first before coin prices move ㆍWhether whales are accumulating or dumping ㆍHow to read market sentiment through bridge and stablecoin flows ㆍHow to extract data via Etherscan and Dune without programming ㆍAnd the interpretation patterns and strategies used by professional analysts This course is designed to help you go beyond "just looking" at on-chain data and build an analytical framework that leads to actual investment decisions.

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.

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

To design AI semiconductors such as NPUs, transformer accelerators, and mini LLMs yourself, you need to understand theoretically why the computations inside them occur. This course distills and condenses into three parts only the essential theory required for hardware design, covering everything from a single neuron to CNNs, transformers, and LLMs. Rather than memorizing formulas, a professor with 30 years of experience in the semiconductor industry connects software theory with hardware implementation through an intuition for “why it works that way.”

This is a lecture on how to use NumPy and practice its application in real-world scenarios.

Do you know the theory but feel lost when applying it to real-world data? I will pass on practical techniques to solve complex fraudulent transactions directly with code, incorporating my professional know-how.

Aim for a quick pass of the first nationally certified AI qualification! Includes three sets of practical problem-solving exercises reconstructed by the instructor after taking the exam personally, along with code templates.

🚀 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.

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 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.

This is a practical course on 'AI-based Circuit Design/Verification Automation' as required by professionals at Samsung Electronics and SK Hynix. Master the skills for LDO IP design and verification automation based on TSMC 180nm PDK, AI-based AXI-Lite RTL implementation, and Python/TCL/Batch script regression automation.

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 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.

If you want to be recognized as a machine learning expert in large-scale data environments—from understanding the core framework of Spark Machine Learning to SQL-based data processing through high-difficulty practical problems, and the ability to implement optimized machine learning models through business domain analysis—join this course.

Understand the core of big data business and provide insights on how to utilize it in marketing strategies in the field.

Through this course, you will learn about SQL and databases, SQL syntax, database design for practical use, how to use databases through programming, and real-world applications of data analysis.

- 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]

As AI technology advances rapidly, it is becoming increasingly important to understand how AI works, learns, and makes decisions—not just how to use AI tools. This comprehensive course is designed to provide a structured introduction to the fundamentals and operating principles of AI, from data training and decision-making to prediction, AI service development, and problem-solving. Using a range of examples and analogies, the course explains how AI learns from and processes data, how it generates results and makes decisions, and how AI services are developed and structured as complete systems, making these concepts accessible even to beginners. The course also explores the challenges and limitations that can arise when applying AI in practice, and introduces basic approaches to analyzing and addressing them. By the end, learners will have a comprehensive understanding of the full process, from how AI works to how services are developed and problems are solved. They will also build a structured way of thinking about AI and a foundation for applying it in practice.

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.

Here are some key competencies that are often overlooked by others in the fiercely competitive and complex semiconductor process job market.