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

Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.

This course covers the process of automating algorithmic trading in local and cloud environments, and focuses on hands-on practice.

You can systematically learn stock trading automation using Python and the Interactive Brokers API, which holds the top spot in global market share.

Learn a systematic approach to S&P 500 pair trading with Python. Lay the foundation for an investment strategy that excludes emotions through professional data analysis.

Docker Compose always appears when you actually try to use Docker! You can learn what it is good for and how to use it through practice.

Let's visually learn the principles of deep learning using Excel.

Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.

When you use Pandas for data analysis, preprocessing, machine learning, and deep learning, you tend to use only the functions you use frequently. There is a Pandas Cheat Sheet that collects and organizes only these essential contents. For those who get tired of learning Pandas from a thick book, we will teach you Python Pandas with just two pages of documents. Learn the core functions of Pandas with the cheat sheet provided in the official Pandas tutorial.

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

This is a Docker guide for beginners who don't know anything about Docker. We focused on helping you quickly learn why Docker is popular and how to use it, without getting into complicated details.

This is not a lecture that simply memorizes commands. It's a practical introductory course that helps you intuitively understand the 'flow' of technology - why containers emerged, how Docker creates and runs images based on what principles. Even if you're encountering Docker for the first time, after taking this course, you'll be able to explain "why containers are a necessary technology" on your own.

This is a course that intensively covers gradient descent, the most fundamental learning algorithm in deep learning.

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 implement a web service modeled after Pinterest's unique card-type layout design using Django, Python's representative web framework.

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!

Digital marketing that makes money, such as Naver Shopping and Coupang Partners, requires consistency, but you can approach it easily and effectively with scientific analysis and automation. We will teach you the key secrets of creating a program that collects and analyzes popular products, keywords, blogs, and shopping information.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

Through this lecture, you can learn about understanding SQL and databases, SQL syntax, database design for practical application, database utilization through programming, and even data analysis application cases.

It explains almost everything surrounding the cloud in an easy-to-understand manner. If you listen to it before the actual practice, you will be able to understand development or infrastructure cloud well.

This training is a hands-on course covering infrastructure construction using Docker container technology, vulnerability analysis, and API web server diagnostics. It is designed to comprehensively learn from the basic concepts of Docker to its application in real-world environments, security vulnerability analysis, and response.

What to Expect from This Course Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life. Course Highlights State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents. Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn. Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns. Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker. By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents. With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!

Revolutionize your stock investments with AI! In this lecture, you will directly build a real, working AI automated trading system targeting the top 25 NASDAQ stocks. We provide a special opportunity to gain experience and know-how through simulated investments with the 한국투자증권 API, and to perform AI stock automated trading with a real account. Even stock beginners will have no problem. We will easily explain basic stock concepts and why you should invest in US stocks, and provide all source code and remote support. From now on, investment is essential for the next 60 years of our lives. Create your own AI investment partner that will be with you!

In Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.