Understanding the Latest Big Data and Artificial Intelligence Trends

Do you want to take your first step into the world of data? Learn machine learning and deep learning, the core technologies of AI, with Python. This course will guide you step by step, from the basics of machine learning and deep learning to practical applications. Traditional machine learning and deep learning are based on many of the same principles and technical systems. Therefore, this course does not separate the two into separate subjects, but rather organizes them into one connected course so that beginners can increase their understanding of machine learning as a whole.

Learn how to build deep learning models directly through intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

This course is a step-by-step learning process that covers the principles of image and object recognition using deep learning, from fundamentals to the latest models. - Building Foundations: Understanding tensors and basic neural network structures with PyTorch - Understanding Images: Learning computer vision concepts, image data structures, and augmentation techniques - CNN Model Training: Hands-on image classification practice with Convolutional Neural Networks (CNN) using datasets like CIFAR-10 - Transfer Learning: Leveraging pre-trained models to achieve fast learning with limited data - Object Detection: Understanding and practicing with the latest object detection models including R-CNN, YOLO, SSD, DETR - Segmentation: Experiencing pixel-level object segmentation through U-Net and Mask R-CNN

Understand the operating principles of deep learning-based generative AI models and acquire utilization methods through hands-on practice.

In the modern business world, data security and fraud prevention have become more important than ever. This lecture focuses on how to effectively detect and analyze fraudulent transactions using artificial intelligence (AI). Using Python and machine learning, we will build an AI model that can detect and detect anomalies in various industries, such as fraudulent transactions, credit card fraud, and production line abnormalities, early.

Are you new to coding or looking to take your coding skills to the next level? This course will introduce you to Python, a powerful and flexible language, designed for all levels of learners. Taught in a “follow-along” fashion, this course will allow you to learn by actually writing and running code. You’ll discover why Python is one of the most popular programming languages. From artificial intelligence to web development, learning the Python language offers endless possibilities, and it’s an opportunity to greatly expand your personal potential.

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice. Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop. Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios. Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services. 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph 👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo" 👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

This course is a hands-on program for implementing generative AI applications based on text, images, voice, and documents using the OpenAI API. Starting from setting up the Anaconda and Jupyter Notebook environment, it covers essential development environment configurations for practical work, including API Key management and understanding costs and tokens. Based on the latest Responses API, you'll implement text generation, summarization, classification, Vision (image understanding), voice processing, and PDF input processing, while practicing core features used directly in the field step-by-step, such as Function Calling, Structured Outputs (Pydantic), Embedding, and RAG (File Search). Additionally, including Web Search, Code Interpreter, Streaming, Background tasks, and Conversation State management, you'll learn how to expand beyond simple API calls to 'intelligent AI services'. Finally, the goal is to implement agent-based AI systems that autonomously select and execute tools using the Agents SDK and MCP (Model Context Protocol), while learning the structure and design perspectives necessary for actual service development.

This course covers everything from the basic concepts of recommendation systems to the principles of applying deep learning. Learn about various recommendation algorithms such as collaborative filtering, content-based filtering, and hybrid recommendation systems, and develop practical skills for developing recommendation services!

Financial data such as stocks, exchange rates, coins, real estate, and bonds are easily accessible and provide us with opportunities to make money, but most people miss valuable opportunities because they do not know their characteristics and how to handle them. Through this lecture, you can learn how to understand the meaning of various financial data and how to analyze them using Python.

All the recent amazing achievements in the field of artificial intelligence have been announced in the field of reinforcement learning. We have covered reinforcement learning technology, which is revolutionizing true artificial intelligence technology such as robots, autonomous driving technology, and human-like machines, from the basics to the advanced level, in an easy-to-understand way for beginners.

Natural Language Processing (NLP) is one of the fastest growing areas of artificial intelligence. This course covers a wide range of topics, from the basics of NLP to the latest NLP techniques using deep learning. In particular, it provides an in-depth understanding of cutting-edge generative models such as ChatGPT.

You can acquire an overview of machine learning and deep learning, how to use basic tools, and the Python language knowledge necessary for learning in a short period of time.

JavaScript, which is known to all web developers, now explore the world of machine learning with this powerful language! This course learns how to build and deploy machine learning models using JavaScript and the powerful machine learning library Tensorflow.js. It guides you step-by-step through all the technologies required to develop web-based machine learning applications. Through this course, learners will systematically understand the core principles of machine learning. In addition, you will learn how to develop deep learning models using JavaScript and Tensorflow.js APIs, how to utilize transfer learning based on pre-trained models, and how to apply all this knowledge interactively in a browser environment.

**"AI Philosophy - Ethics and Challenges in the Age of Artificial Intelligence"** is a topic that deals with the ethical and philosophical exploration of the development of artificial intelligence (AI) and its impact on human society and culture. This topic goes beyond simple technological development and delves into how AI affects human life and what ethical decisions we should make accordingly.

Without a single formula or line of code, this penetrates the essence of basic statistics necessary for AI development and application.

An introductory AI lecture tailored for those new to AI! Key concepts, from data to generative AI, are simply explained. The goal is practical understanding, enabling you to question and explain AI, not build it.

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!

Create high-quality AI videos with Midjourney&Runway. Director Kim Dong-ah, who collaborated with Samsung, will personally guide you step-by-step through the AI video production process based on a virtual brand.

You can transform your ideas into beautiful presentations in just a few minutes. No more spending hours struggling with slide design. With GAMMA's AI technology, you can dramatically improve your content creation process.

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

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 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 comprehensively learn everything from establishing AI-based system test strategies to designing and executing test cases, and even methods for utilizing AI to support testing. This course is conducted based on ISTQB's AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

Using block coding Entry, foster computational thinking and problem-solving skills, and through Entry AI tools, learn artificial intelligence concepts and practice simple machine learning.

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

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.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

AI is no longer a tool exclusive to developers or experts. The gap between "those who use AI" and "those who don't" is already widening across all roles—from students and job seekers to office workers, planners, and developers. This course is a practical survival guide focused on: ✔️ How to use AI as a tool to explode your productivity, not as a replacement technology ✔️ Prompts you can immediately apply to work, studies, job hunting, planning, and development ✔️ The mindset that turns someone who says "I tried AI, but it wasn't that great" into an AI expert Rather than just explaining features, I will show you: 👉 What kind of people survive in the AI era 👉 Exactly how those people use AI in practice Based on my own experiences and real-world examples.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

This explains the foundational principles of large language models like ChatGPT with a focus on theory.

This course covers the entire process of evolving ChatGPT from a simple conversational AI to an intelligent AI Agent that performs actual work tasks. Using three core frameworks - LangChain, LangGraph, and LangSmith - you'll directly create AI Agents that can be immediately used in real-world applications.