Practical Docker: Creating your own deep learning cloud computer with Docker

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

Let's learn the principles of deep learning by visualizing them with Excel.

The lecture focuses on how to use Docker in Swarm mode. It guides beginners to easily build and operate servers.

In this course, you will acquire basic knowledge about Azure virtual machines and learn how to design and manage virtual machines according to workload.

This is a lecture on creating and deploying your own GPT using various resources in Azure. From start to finish! Let's deploy a complete service from scratch!

Systematically learn Docker, a core tool for container-based application deployment, from the basics to practical application. Starting with Docker's basic structure and commands, you will develop practical skills that can be immediately applied to real-world tasks, including container packaging in microservices architecture environments, deployment automation, and standardization of production environments.

I want to classify pictures of pizza and spaghetti! This is a deep learning practical project course that follows the A to Z of a practical project to classify images using CNN, one of the deep learning algorithms.

When using Pandas for data analysis, preprocessing, machine learning, and deep learning, we often find ourselves relying on the same core functions. There is a Pandas Cheat Sheet that compiles only these essential features. For those who have grown tired of trying to learn Pandas through thick books, this two-page document will teach you Python Pandas. Master the core functions of Pandas using the cheat sheet provided by the official Pandas tutorial.

This is a comprehensive course on Keras theory and practice that uses Keras and Python, the common language of artificial intelligence, to learn various useful machine learning regression, classification, and deep learning neural network projects along with theory.

There was a rumor that Ediya Coffee always opens its stores near Starbucks. How much of a difference is there really between the store locations of Ediya and Starbucks? Will the real estate price fluctuation trends from 2013 to 2019 be reflected in apartment pre-sale prices as well? What kind of parks are in my neighborhood? How can we best utilize the data available on the Public Data Portal? The goal is to become familiar with Python and various data analysis libraries by working with different types of data through public data.

You can quickly experience the process of deploying a sample e-commerce service to AWS and continuously improving it into a robust and efficient environment.

Beyond traditional Jenkins-based CI/CD, this course also covers GitOps deployments using ArgoCD. You will design and implement CI/CD and GitOps deployment strategies for dev, staging, and production environments by combining Jenkins and ArgoCD. You will work with both push-based Jenkins and pull-based ArgoCD, directly comparing the two approaches on the same cluster to build a practical hybrid deployment pipeline. You will practice essential deployment techniques for operations, including Canary, Blue-Green, RBAC, approval procedures, incident response, and automatic rollback.

For learners who find Docker operations and security hands-on exercises daunting, the instructor guides them through real-world workflows based on their infrastructure and penetration testing experience.

After learning SQL tuning, automate recurring tuning tasks. This hands-on project connects everything from collecting MySQL queries to AI diagnosis, empirical validation, handoff to the person in charge, and tracking recurrence.

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.

This is a practical, hands-on course where you will learn the principles of AI Agents—going beyond simple chatbots that just answer questions to systems that can perceive situations, make judgments, and control actual systems. The curriculum is structured to provide a step-by-step understanding, starting from the differences between AI Agents and chatbots to the core Agent Loop structure: Sense-Think-Act, state management, planning, and feedback loops. Moving beyond theory, you will set up Windows and Linux development environments and gain hands-on experience with the basic structure of Kubernetes and kubectl commands. Following this, you will implement a process where an AI Agent detects server issues, analyzes the causes, and automatically resolves errors. Finally, by directly analyzing the Python code and internal logic of the Agent controlling Kubernetes, you will understand how AI Agents connect to and operate within real-world systems. From developers new to AI Agents to those who want to design Multi-Agents that control actual systems, this course allows you to learn both the principles and the practical implementation process of Agents.

Instead of long and boring theories, you can learn them one by one through short practical exercises, and as you flesh them out one by one, you will soon be able to experience the overall architecture composition.

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.

Revolutionize your stock investing with AI! In this course, you’ll build a fully functional AI automated trading system that targets the top 25 Nasdaq stocks. Gain experience and know-how through paper trading with the Korea Investment & Securities API, and take advantage of this special opportunity to conduct AI-powered automated stock trading with a real account. Even if you’re a complete beginner, there’s no problem. We’ll explain the basic concepts of stocks and why you should invest in U.S. stocks in an easy-to-understand way, and provide all source code along with remote support. From now on, investing is essential for the next 60 years of our lives. Build your own AI investment partner to accompany you!