From an overview of artificial intelligence to its history

I got answers from AI, but am I still doing the work myself? From the basics of using Claude Code to building apps, automating Instagram carousel posts, and researching competitors’ social media. Follow along while watching the screen and learn how to apply it to your own work. This includes 11 beginner-focused desktop app lessons, along with hands-on practice for real-world tasks.

👉 A starting point for those who want to properly understand AI for the first time. If you understand what AI does but aren’t quite sure why it works the way it does, this is the course to start with. In this course, we’ll explain step by step, from the beginning, how artificial intelligence differs from traditional programs and how learning and decision-making take place. Without complicated equations or coding, the course is designed to help you understand the big picture of how AI works.

This course covers the basics of using SearchGPT, OpenAI’s AI-powered search tool, as well as its various features and applications. You will learn how to use and apply its text-based functions through prompt input.

Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

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.

- 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]

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.

This course is for those who are familiar with deep learning terminology but feel overwhelmed by how models learn. Across 13 lectures, it connects and explains the key principles, from the fundamentals of linear models and neural networks to backpropagation, optimization, CNNs, RNNs, and transformers. You will build a solid foundation for studying deep learning systematically by understanding model architectures, the learning process, and the factors that affect performance.

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.

An introductory AI course designed for beginners! It clearly explains key concepts, from data to generative AI. The goal is to build practical understanding—not to create AI, but to reach a level where you can ask questions and explain the concepts.

Processing actual smart health data held by Daegu Haany University for use in machine learning, and creating customized smart health applications using vibe coding and refined information.

- I am a Research Engineer/AI PM running a 4-year-old AI startup. - This video is a lecture for those who want to start studying artificial intelligence "now" or in "2026". - Looking at college students, I see many who have no idea how to get started with artificial intelligence. - I hope that after watching this lecture, you'll be able to start studying artificial intelligence. - For reference, this lecture is not about "using AI in my work" or "getting started with monetization methods using GPT". - This is a video about how students, graduate students, or developers can get started when they want to study artificial intelligence.

Delegate the pressure of overflowing financial data and reports to an AI agent. From corporate analysis to report automation, an overwhelming workflow for financial experts that changes the game of practical business!

There may be people who have never taken my lecture, but no one has taken it only once. After teaching in person for so long, I have finally had the opportunity to create this online course. Since the commercialization of AI, I have produced and researched countless pieces of AI content. Based on that knowledge and experience, I have built a logical philosophy of my own and organized it into concepts that are easy for anyone to understand. For those who have been generating AI images without considering the underlying concepts, I will instill a proper foundation in you. Once you take this course, you will be able to create any AI image in the future. Have you never created an AI image before? That’s perfectly fine. You don't need any prior experience in AI image generation. By simply learning and practicing through this lecture, you can become an expert in AI content creation.

If you’ve been leaving work late because you had to handle everything alone—from organizing meeting minutes, announcements, proposals, presentation materials, and Excel files to customer inquiries—now leave it to Claude. I’ll show you how to complete 11 practical tasks one by one, save them as skills, and automate your daily work, using the exact way I use it myself as a non-developer.

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will gain a broad range of knowledge, from establishing AI-based system testing strategies to designing and executing test cases, as well as utilizing AI for test support. This course is based on the ISTQB AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

We transform distribution reports that merely list numbers into a single page that supports decision-making. Drawing on 24 years of experience in distribution operations and headquarters, we use Excel and AI to analyze changes in sales and contribution margin and examine hypotheses about their causes. By calculating simulated distribution data ourselves, we complete performance reports containing key conclusions, execution conditions, and approval requests.

Stop getting student-record planning. I’ll reveal the entire consulting playbook used by a college admissions consultant with 30 years of experience.

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

When you learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.