[リニューアル] 初めてのPython機械学習ブートキャンプ（簡単に！実際のKaggle問題を解きながら整理する）[データ分析/科学 Part2]

This course offers an easy and fast way to learn FastAPI, the latest full-stack and backend framework, along with cleanly organized materials and projects that cover backend concepts, core FastAPI syntax, and asynchronous database utilization in an easy, step-by-step manner.

This course aims to teach data analysis beginners fundamental skills for the entire Python data analysis workflow. As an e-commerce planner and developer leveraging real-world data analysis, I crafted this for easy acquisition and immediate application of the full Python data analysis process.

For those looking to learn Data Science, Big Data, and Crawling, (1) we will summarize the most essential Python core syntax in a short time, and (2) you will become familiar with Python and crawling through practical crawling programming.

This newly designed course, drawing from the instructor's initial deep learning failures, helps you progressively learn deep learning essentials: math, theory, PyTorch-based implementation, transfer learning, and GPT's core Transformer.

This course, Part3 of the Full-stack Course Series, solidly teaches the latest server and Docker technologies. It's structured to practically integrate Docker into your tech stack, meticulously testing its options, learning necessary related tech like Linux, and building complex services.

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.

This is Part 1 of a roadmap lecture that concisely organizes the essential computer science (CS knowledge) needed to excel in development and data, in a short period. This lecture has also been used as an in-house lecture at top domestic companies for several years. Based on 20 years of experience in actual practice, from system SW to web, app development, and data science, this lecture systematically yet easily organizes the essential core knowledge needed to excel in development and data.

Based on real-world web service development experience, this full-stack series targets beginners, aiming to develop web and app services capable of actual commercialization. As the initial step (part1-1), this lecture details intermediate Python for full-scale development, backend implementation with flask, and essential web technologies (e.g., HTTP, RestAPI, MVC patterns).

Learn NoSQL technology, which can handle Big Data, one of the fundamental technologies in full-stack and data science utilized by modern startups. MongoDB is the easiest and quickest technology to utilize among NoSQL databases. In this lecture, you will learn the basics of MongoDB in a short time, and also acquire the skills to manipulate and utilize MongoDB with Python.

Master advanced SQL, solve 101 practical data analysis problems at a professional level together, and build a solid SQL data analysis skillset.

This is a full-stack lecture series based on real-world web service development experience, aiming to help beginners develop web and app services to a level suitable for actual commercialization. As Part 2 of this series, this lecture thoroughly and solidly covers the latest HTML5, CSS3, vanilla Javascript + ES6, considering responsiveness and compatibility within the diverse landscape of frontend technologies.

This comprehensive course allows step-by-step Flutter learning, from Dart programming basics to Flutter's basic/intermediate features, and integrating backend Firebase for real projects.

This course aims to teach data analysis beginners fundamental skills for the entire Python data analysis workflow. As an e-commerce planner and developer leveraging real-world data analysis, I crafted this for easy acquisition and immediate application of the full Python data analysis process.

This newly designed course, drawing from the instructor's initial deep learning failures, helps you progressively learn deep learning essentials: math, theory, PyTorch-based implementation, transfer learning, and GPT's core Transformer.

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.

In this lecture, you will learn the basic theory of reinforcement learning.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

I will guide non-majors and beginners to quickly pass the Big Data Analysis Engineer practical exam! Theory lightly, practice thoroughly. Even without complex background knowledge, focusing on past questions, we intensively study only the points sure to appear on the exam.

Learn NoSQL technology, which can handle Big Data, one of the fundamental technologies in full-stack and data science utilized by modern startups. MongoDB is the easiest and quickest technology to utilize among NoSQL databases. In this lecture, you will learn the basics of MongoDB in a short time, and also acquire the skills to manipulate and utilize MongoDB with Python.

Make AI data analysis and modeling results into a Web Program.

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

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.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

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.

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.

Master the Big Data Analysis Engineer certification, an essential but never easy-to-self-study credential, in a short period by utilizing R, an easy-to-learn data analysis tool!!