Preparing for AI Graduate School A-Z: From Spec Preparation to Contacting Professors, Application Documents, and Interviews

Introducing AI entry-level study methods for undergraduates.

Occupational health instructors are divided into two fields: occupational and environmental medicine and industrial hygiene.

Studying to become an occupational health instructor is a comprehensive art. It includes medicine, nursing, health science, business administration, and engineering.

- I am a Research Engineer/AI PM running a 4-year-old AI startup. - This video is a lecture for those who want to start studying artificial intelligence "now" or in "2026". - Looking at college students, I see many who have no idea how to get started with artificial intelligence. - I hope that after watching this lecture, you'll be able to start studying artificial intelligence. - For reference, this lecture is not about "using AI in my work" or "getting started with monetization methods using GPT". - This is a video about how students, graduate students, or developers can get started when they want to study artificial intelligence.

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.

Find your way through the sea of data and explore the future of artificial intelligence. This course explores the two massive technological trends of Big Data and Artificial Intelligence (AI), examining how they are bringing innovation to our lives and work.

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

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.

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!

Do you want to generate significant profits even in a bear market? Are you interested in automated trading but haven't been able to start because of coding, trading strategy design, exchange APIs, or server deployment? In this course, we guide even those new to Python to turn trading ideas into concrete strategies using ChatGPT and Codex, and implement them into backtesting and live automated trading code. We will practice Binance Futures automated trading using Python and CCXT, technical indicator strategies, DCA (dollar-cost averaging) backtesting, and Upbit API-based Kimchi Premium strategies. Afterward, we will apply Telegram notifications and order safety features, manage the completed bot with GitHub, and deploy it to a cloud server. Rather than leaving all investment decisions to the LLM, humans will determine the strategies and risk management criteria, while using the LLM as an assistant to help with strategy design, coding, and troubleshooting. After completing the course, you won't just be running the provided code—you will be able to create, verify, modify, and deploy your own unique strategies!

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.

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

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 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.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

The power of AI, completed with PPT. This course goes beyond simple presentation creation, It's an all-in-one course that uses AI to finish everything from planning to design and content creation at once. Complex design? Now AI suggests everything from the initial draft Product detail pages, video production, carousels, GIF banners, e-books, etc. all created with just PPT AI prompt-based content generation followed by content creation with PPT, etc. Complete disclosure of AI+PPT's unique workflow! Even if you're not a designer, even if you don't know the tools well A practical course where anyone can create professional-level results. 💡 "You provide the ideas, AI and PPT handle the rest!" Right now, your content creation method will completely change.

Many people use ChatGPT and frequently hear about recommendation systems and autonomous driving, but when asked to explain “why they work the way they do,” they often struggle to find the words. If you only follow coding courses, the code runs but the underlying principles don’t stick. If you only take math courses, you see the formulas but can’t connect them to AI. Overseas courses can feel intimidating because of the English, while introductory courses in Korea tend to focus heavily on hands-on practice. While building real models in AI competitions, running Everyone’s AI, and meeting thousands of learners, I repeatedly saw the same thing: the obstacle wasn’t how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation became disconnected from one another. This course bridges that gap. From how functions define inputs and outputs, to how derivatives determine the direction of learning, and how backpropagation passes errors backward, it explains everything in one continuous flow—from the fundamentals of mathematics to neural networks and softmax. Rather than memorizing formulas or simply copying code, this course helps you understand “why” through equations and intuitive illustrations. For anyone who wants to truly understand AI, it will serve as a solid first step toward moving on to deep learning.

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

Customer analysis completed with Excel and Orange, from cross-selling to A/B testing and churn risk management all at once

For those who want to enjoy Go even more by improving their skills beyond the beginner level! Study the essence and direction of Go, which always felt difficult, with Pension Go! Completed in 50 days! The "Learning Go through Review: Intermediate 2" course (6-kyu to 4-kyu) is a lecture designed to help those who have moved past the Intermediate 1 stage to master the essence of Go and increase their playing strength.

A Non-Major Developer's 5-Year Journey: A Guide to Reducing Time and Trial and Error