Basic Math and Statistics for Non-Majors (Practice)

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 essential to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence!

Beginner's Guide to Creating Your First Game Using Only Blueprints Without Coding

A hands-on project course for creating AI NPCs capable of real conversations by integrating Unreal Engine 5 with OpenAI API

Understand the structure of the network framework provided by Unreal Engine. Understand what servers, clients, replication, and RPCs are and how they work. You can create network games using the Unreal Framework.

Learn how to use deep learning with Python and Keras.

The language that uses the Unity engine is C#. The lecture content mainly covers frequently used functions in Unity, including data types and conversion, object orientation and generalization, and even simple file input/output!

Establish your own job search strategy based on recruitment trends and job analysis. A to Z job search preparation from self-introduction to interview

We will explain the basic structure of what Linux is, learn how to handle the shell, and learn to gain a basic understanding of Linux networks.

Python Programming for Non-Majors

Do you need art work for 3D printing or interior design and games? Create realistic 3D graphic design works with Blender Basics course.

You can learn about the basic structure and operating principles of computer hardware, design methods, and OS, and acquire the knowledge necessary to create good programs.

It lays the foundation for basic data analysis, including understanding R and data, data processing methods for data analysis, and data visualization methods. You will learn how to conveniently process data into the shape you want by utilizing variables, functions, and packages.

This lecture will teach you how to systematically analyze and organize the core functions and conditions of software, and explain the theoretical background of its relationship with testing for finding and improving bugs based on this.

To create VR/AR content using the Unity engine, you must learn C#, and to learn C#, you must learn the C language. This course covers everything from the basics of the C language to memory management.

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 essential to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence!

Understand machine learning concepts, AI learning principles, and mathematical principles for model implementation to fully utilize deep learning!

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

Data analysis sounds a bit grandiose, but it is actually a process of establishing a hypothesis and verifying it based on data. You can learn how to analyze what data to verify your hypothesis using Python.

This course makes it easy to learn the essential mathematical concepts used in games. People who are already proficient in math often take this knowledge for granted, making it difficult for them to teach from a beginner's perspective. This course assumes you have no prior knowledge and explains everything from the viewpoint of a complete beginner or non-major.

Basic knowledge is not enough for real-world investing; build your own profit model using the machine learning know-how of a 10-year veteran quant.

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

This is a basic Python Excel programming course learned through an xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

"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.

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.

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

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 guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

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.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.

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.