[AICE] Customized lecture for passing the Basic level

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

Understanding programming concepts is no longer an option, it's a must! Are you flustered by a sudden collaboration with developers? Do you want to start a career as a developer? Programming, which felt difficult as a non-major! Let's go from concepts to basic practice together.

1. Mock exam problem-solving for passing AICE Associate, Korea's only state-certified AI certification. 2. A course featuring 12 mock exam sessions with the same question types as the actual AICE Associate exam.

This is a basic machine learning course for those who don't know math and coding well, but want to understand machine learning trends and utilize machine learning technology to plan products.

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.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

This course is designed to prepare you for the Information Processing Engineer practical exam solely through solving previous exam questions. Instead of unnecessary theories or vast amounts of content, it focuses on past exam questions that are repeatedly featured, allowing you to connect your learning directly to passing the exam. You can quickly grasp core points and question patterns by reflecting on the latest trends, and even learn tips for writing answers through the problem-solving process. This lecture is optimized for examinees aiming to pass efficiently in a short period of time.

It's okay if you don't know Python! This course is a hands-on lecture designed so that even those who are new to programming can follow along. By creating the classic game Space Invaders yourself, you can naturally learn Python basic syntax and 2D game-programming using Pygame. Since you learn while making a game, there's no time to be bored, and you can feel the sense of accomplishment of completing something with your own hands.

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

This is a lecture for people who are starting development for the first time. It's about programming thinking, which should come before development languages or UI frameworks.

How to easily set up git, VS Code, Node.js, Python, Java dev environments on Windows within 2 hours without struggling.

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.

We foster capabilities to prepare for future technological changes by learning from the basics related technologies that are at the core of 4th Industrial Revolution technologies, such as ICBM (IoT, Cloud, Big Data, ML), autonomous vehicles, and the metaverse.

News crawling in the automation lecture... Are you bored? This is a lecture on developing an automation program that will make you an Instagram influencer.

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.

I used to give up after studying only Python before even starting to study AI. Don't waste your energy learning every single feature you might only use once in a lifetime; I will teach you only the essential content you truly need to know.

Do you want to automate repetitive tasks with AI without using paid tools? I will share the practical know-how I gained from building systems myself using n8n and Llama. This is not a lecture where you simply learn "how to connect nodes." We will build actual workflows that you can use immediately, such as automatic Google Sheets organization, YouTube subtitle collection, AI fact-checking, and schedule management.

ADsP Certification All-In-One Course! Ultra-Short Completion! Reference Book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

Regardless of the field! This is a lecture that covers the core fundamentals that developers must know in practice in an easy + code-oriented way. I highly recommend this lecture to all junior developers and job seekers who want to grow.

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.