Data Science(DS) and AI Learning & Career Guide for Non-Majors/Beginners

We will learn step by step from the basic concepts of LLM (Large Language Model) to how to fine-tune the Llama 2 model, a high-performance LLM, on the dataset of your choice.

AI Agent: A total integration of the latest AI technology! Implement various AI agents and learn to build your own AI agent using LangGraph.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

This is an all-in-one lecture where you can learn the entire process from the basics of deep learning/TensorFlow/computer vision to practical applications through a practical vehicle license plate recognition project. Through various practical exercises, you can develop practical skills to apply the latest deep learning models to custom datasets.

This lecture covers building practical RAG (Retrieval-Augmented Generation) systems while developing an AI customer service chatbot (AICC).

This course teaches you how to implement deep learning papers by implementing the YOLO (You Only Look Once) paper from scratch using TensorFlow 2.0.

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

This lecture teaches various business automation methods using ChatGPT (Excel, PPT, crawling, image processing) and Python basics for business automation. Experience in advance the changed future that ChatGPT will create.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

Learn all the steps from A to Z required to create and operate a cryptocurrency automatic trading coin bot. (Basics of using exchange APIs, deployment using AWS) You can also learn investment strategies (such as using Kimchi Premium) that can generate risk-free returns of around 10% per year by utilizing the structural characteristics of the cryptocurrency market.

This is a lecture where you build your own MCP server using Cursor and experience vibe coding, which embodies the future of developers.

This course will teach you the core theories of deep learning and how to implement deep learning code using the latest TensorFlow 2.0.

This course covers data analysis using ChatGPT and Pandas utilization. Get a preview of the changed future ChatGPT will create.

From the basics of deep learning natural language processing to the latest models such as Transformer and BERT, learn the principles and utilization methods of deep learning natural language processing (NLP) through various examples and practical code implementations.

This is a lecture where you can learn how to use Git & GitHub step by step from the basics to advanced content through various practical exercises. Understand the principles through intuitive examples and learn how to use Git & GitHub through practice.

This course teaches you how to perform Object Detection using the latest high-performance deep learning model using the TensorFlow Object Detection API through simple Colab exercises, and learns the principles of the latest deep learning Object Detection models.

A course to learn website crawling methods using ChatGPT. Get a preview of the transformed future ChatGPT will create.

This lecture teaches how to create diverse quant investment strategies using ChatGPT. Use ChatGPT to build your own quant investment strategy.

This is a lecture on creating an automatic stock trading bot using the IBK Investment & Securities Xing API. Create your own automatic stock trading bot that trades automatically 365 days a year.

A general education lecture for the public to shed the vague fear of artificial intelligence. Through concrete examples, we learn what AI learning truly means. Also, via specific examples, we examine AI's 'brain structure' and how it thinks.

This is a lecture for non-majors or high school students who are studying data science and artificial intelligence for the first time. We will teach you the concepts of artificial intelligence and machine learning and what efforts you need to make to get a related job.

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.

A comprehensive compilation of approximately 130 ML/AI engineer interview questions and answers learned through about 20 interviews

"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!

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.

We understand why Attention was needed and how it works by 'implementing it directly with code'. This lecture starts from the structural limitations of RNN and Seq2Seq models, experimentally verifies the information bottleneck problem and long-term dependency issues created by fixed context vectors, and naturally explains how Attention emerged to solve these limitations. Rather than simply introducing concepts, we directly confirm RNN's structural limitations and Seq2Seq's information bottleneck problems through experiments, and implement **Bahdanau Attention (additive attention)** and **Luong Attention (dot-product attention)** one by one to clearly understand their differences. Each attention mechanism forms Query–Key–Value relationships in what way, has what mathematical and intuitive differences in the weight calculation process, and why it inevitably led to later models naturally connects to their characteristics and evolutionary flow. We learn how Attention views sentences and words, and how each word receives importance weighting to integrate information in a form where formula → intuition → code → experiment are connected as one. This lecture is a process of building 'foundational strength' to properly understand Transformers, helping you deeply understand why the concept of Attention was revolutionary, and why all subsequent state-of-the-art NLP models (Transformer, BERT, GPT, etc.) adopt Attention as a core component. This lecture is optimized for learners who want to embody the flow from RNN → Seq2Seq → Attention not through concepts but through code and experiments.

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

ADsP Certification All-In-One Course! Ultra-Short Term Completion! Reflecting 2025 August 46th Exam! (Continuous Updates!)

Learn and understand AI and AI ethics, then integrate with your major to boost AI digital skills!

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.

Do you want to become a data analytics expert of the future? Grab your chance now! This is a unique opportunity to explore infinite possibilities and learn how to derive powerful insights through cutting-edge artificial intelligence technology and big data.

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.

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

This is a lecture to build an AI portfolio by doing a clone project of airbnb data science team's Amenity Detection project, an excellent TensorFlow utilization case, and organizing the experience on a GitHub blog.

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 :)