[PY 0202] Python Level 2 for Artificial Intelligence

We will summarize the theoretical concepts of MNIST, the most fundamental dataset in computer vision, and learn how to implement it using Torchvision's API as well as how to implement a custom dataset class directly.

Build a solid theoretical foundation of images essential before learning deep learning-based computer vision. Understand the essence of image data, such as pixels, resolution, and color representation, and cultivate practical programming skills to read, transform, and preprocess images using Python.

This is a lecture that condenses the mathematics curriculum taught in middle and high school. You will learn the necessary mathematical knowledge and techniques through extensive practice for each item.

This is a lecture that provides a brief introduction to the field of computer vision.

This course provides a complete theoretical overview of the operations of convolutional and pooling layers, which are the core modules of Convolutional Neural Networks, and helps you develop implementation skills by building them yourself.

This course teaches you how to use LaTeX to write formulas.

Limits of functions, derivatives, differentiation rules, derivative formulas, applications of differentiation

This is the first lecture in linear algebra, and it is a lecture that geometrically understands vectors and vector operations.

This is a course that offers extensive practice in Python's basic syntax. This is a course that provides practical exercises used in Machine Learning and Deep Learning, along with Python's syntax.

This lecture focuses on convolution, the most important operation in computer vision using deep learning.

This is a lecture on implementing practical code using PyTorch features based on the code directly implemented in Gradient-based Linear Regression (1). Additionally, it's a lecture where you'll theoretically learn about the necessity of data preprocessing, its theory, and its impact on learning, and implement it with practical code.

This is a lecture for perfectly understanding the process of training the simplest form of deep learning model using gradient descent.

This is a course that intensively covers gradient descent, the most fundamental learning algorithm in deep learning.

This is a course where you learn the basic calculus and PyTorch's Autograd functionality needed to get started with deep learning.

This is a lecture on data visualization using the Matplotlib library.

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

Learn virtual environment concepts and setup methods.

This is the fifth lecture in the [인공지능을 위한 파이썬] series. It is a course that provides practical exercises used in Machine Learning and Deep Learning, along with Python grammar.

[Python for AI] This is the fourth lecture in the series. It is a lecture that covers Python grammar along with practical exercises used in machine learning and deep learning.

[Python for AI] Third lecture in the series. Learn how to debug Python code.

This is a course that offers extensive practice in Python's basic syntax. This is a course that provides practical exercises used in Machine Learning and Deep Learning, along with Python's syntax.

[Python for AI] This is the fourth lecture in the series. It is a lecture that covers Python grammar along with practical exercises used in machine learning and deep learning.

You can systematically learn stock trading automation using Python and the Interactive Brokers API, which holds the top spot in global market share.

QML is a programming interpreter language for creating UIs provided by Qt. It is similar to JSON and can use JavaScript. Using QML and Python together, you can easily develop modern, advanced UIs.

From the basics of Numpy and Pandas to data preprocessing, visualization, and crawling, all in one place! This is an introductory Python course for data analysis.

Anyone can easily create a Python game using the Pygame library, starting from zero base.

You can easily and enjoyably understand Python programming with games. This is a lecture that even children in elementary and middle school can follow, so parents and teachers will find many places to use it if they learn it. :)

This is a Python coding test lecture centered on problem solving, tailored to those who are new to coding tests.

A lecture consisting of special lectures by professors from Canadian national universities. It covers Python, Code, Scratch programming introduction/basics and practical applications. After completing the lecture, receive a global certificate from Codesprint : )

This course is an AI music generation introductory program designed so that even learners who are new to artificial intelligence (AI) technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and proceed step-by-step through hands-on practice to create your own music using these tools. Going beyond simply pressing buttons to make music, this course guides you to understand the principles of AI models and the key factors that influence music generation, and to complete creative results by adjusting the desired atmosphere, genre, and instrumental composition. Upon completing the course, you will gain confidence in using AI in a friendly manner, along with AI music generation capabilities that can be applied to various fields such as daily life, projects, and content creation.

Learn NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.

For those who want to learn data science, big data, and web crawling, (1) we summarize the most essential Python syntax in a short time, and (2) you'll become familiar with Python and crawling through practical crawling programming.

This course focuses on developing immediately applicable visualization skills by utilizing Seaborn, a core tool for data visualization, and Plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can simultaneously improve your ability to create insight-driven visual materials and implement web-based dashboards.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

AI does the coding, I get the results! Maximize practical programming skills, achievable even for beginners!

In this course, you will learn how to program kernels and develop PyTorch modules. You can use the knowledge you have gained to develop models faster.

Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.

This is a course where you can learn UML and object-oriented design even without programming experience. Concepts are explained through everyday examples (cafes, libraries, shopping malls), and you'll learn 9 types of UML diagrams step by step. You'll develop design skills that can be immediately applied in practice using theory and PlantUML.

This course covers the process of automating algorithmic trading in local and cloud environments, and focuses on hands-on practice.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.