This course aims to develop practical Python programming skills for real-world projects such as deep learning and the Internet of Things. In order to save your time, we have minimized installation and included everything in one course so that you can learn the key content repeatedly.

This is a course that teaches you practical examples that can be used in projects using Cloud Functions, Google's representative Serverless BaaS (Backend as a Service) solution that can be used without a server.

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 machine learning and deep learning project preparation course that uses Python to process Boston Marathon big data into the desired format and uses various charts and technologies to create valuable information.

Easy, Usable Computer Vision Projects: Step-by-step learning of practical CV projects. Boost skill with real examples.

There are many projects that want to provide deep learning programs made with Python as web or mobile services. Learn the process of making Python-based deep learning codes such as TensorFlow, PyTorch, YOLO, OpenCV, and Keras into web service APIs and provide them as mobile web apps, and apply them in practice.

Digital marketing that makes money, such as Naver Shopping and Coupang Partners, requires consistency, but you can approach it easily and effectively with scientific analysis and automation. We will teach you the key secrets of creating a program that collects and analyzes popular products, keywords, blogs, and shopping information.

You will learn how to process images and videos with OpenCV, and how to identify and recognize faces and various objects using deep learning. This is a course where you will learn fun projects such as drowsiness prevention, age and gender identification, counting the number of people passing by, face tracking, and YOLO that finds bad apples and tangerines, along with theories.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

It goes without saying that AI artificial intelligence is the trend. But what can you use machine learning and deep learning for? Now, let's learn useful Internet of Things IoT Computer Vision projects that are used in daily life.

This course is a fun project that learns theories and processes images and videos with OpenCV (Open Source Computer Vision Library) and uses deep learning modules (dnn, Deep Neural Network) to identify and recognize faces, eyes, and various objects.

This is a comprehensive deep learning project course that teaches the latest deep learning techniques, GAN, BERT, RNN, and CNN, while creating various useful projects based on Python and TensorFlow 2 along with theories.

This course is based on Ionic and focuses on a simple but useful petlist project. It will help you develop practical mobile web app development skills in a short period of time.

This is a practical project capacity building course that follows the Pet Store shopping mall Blueprint Application that can be applied to various fields using Angular Front-end and Firebase Back-end.

In this course, you will understand the concepts of data visualization and learn the 5 core uses of charts and 10 core charts so that you can appropriately utilize them in your data analysis projects.

This course aims to develop practical Python programming skills for real-world projects such as deep learning and the Internet of Things. It is designed to save time for busy people and to help them learn key content repeatedly, all in 100 minutes.

This course is based on Vue and condenses the simple but useful petlist project into a 100-minute core lecture, allowing you to develop mobile web app development skills that can be applied in real life in a short period of time.

This course is based on Angular and condenses the simple but useful petlist project into a 100-minute core lecture, allowing you to develop mobile web app development skills that can be applied in real life in a short period of time.

In this course, beginners will learn the basics of Python and develop practical application skills while following interesting projects. - Vending machine program - Missile trajectory tracking program - Introduction to game program PyGame - Mashup service for nearby searches

In this course, you will learn the basics of Raspberry Pi and Python and practice creating simple but essential self-driving car and remote home monitoring projects.

This is a comprehensive deep learning project course that teaches the latest deep learning techniques, GAN, BERT, RNN, and CNN, while creating various useful projects based on Python and TensorFlow 2 along with theories.

This course teaches the “core concepts” of deep learning required to work in the deep learning field and the practical skills required to perform actual deep learning projects through hands-on training using PyTorch.

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.

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 course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

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

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.

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

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.

Get a clear overview of exam types and trends at a glance! Sharpen your practical skills with a total of 120 questions. The perfect preparation for the AWS AI Practitioner exam, designed for systematic learning even when studying alone.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

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.

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.