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.

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

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!

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.

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.

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.

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

This course is a hands-on program where you'll directly implement 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 real-world 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, you'll learn how to expand beyond simple API calls to 'intelligent AI services' by including Web Search, Code Interpreter, Streaming, Background tasks, and Conversation State management. 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.

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.

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!

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.

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

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.

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.

You can benchmark manufacturing innovation strategies for semiconductors and run machine learning and deep learning on semiconductor data without coding.

The AWS Certified AI Practitioner practice exam problem-solving course is a core summary course for certification success. First, you will quickly review the basic concepts of AI/ML and generative AI, and learn the functionalities and use cases of key AWS AI services that frequently appear on the exam (e.g., SageMaker, Rekognition, Polly, etc.). Based on this, you will practice determining which AI solution is suitable for various real-world problem situations, and also cover topics related to the responsible use of AI models and data security. Finally, you will learn problem-solving strategies for each type of past exam question and prepare for the exam through practical mock tests. Through this course, you can efficiently prepare for the exam in a short period.

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.

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.

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

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.

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.

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.

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!

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.

Explains the basic principles of large language models like ChatGPT, focusing on theory.

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.

"LoRA-based lightweight fine-tuning: Your first step to creating a customized LLM!" This course is an introductory hands-on lecture designed so that even those encountering LLMs for the first time can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading models → applying data → training → comparing results. In a short time, you'll directly experience the flow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, gaining an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

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.

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 :)

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.

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.