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ChatGPT, become a creator using generative AI!

Let's learn the basics of the Python programming language and acquire the Python coding skills necessary for artificial intelligence!

Let's understand model algorithms for machine learning and deep learning by utilizing Keras!

Let's understand the main algorithms of machine learning and deep learning, and learn about artificial intelligence!

Let's build a foundation of MATLAB and apply and understand the basic concepts of programming!

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

Let's learn how to translate real-life and major problems into actual computer programs and practice using Python-based artificial intelligence tools!

Let's develop the capacity to extract valuable information from big data and support decision-making!

Let's design, implement, and develop while learning the basic concepts of the C++ language and object-oriented concepts!

Modern C++ programming based on object-oriented concepts for computer science majors

Experience text coding through block coding and control Arduino!

Let's build a foundation for using Python and solve data analysis problems!

Leverage Python to learn machine learning techniques, improving real data information extraction and prediction model development skills!

Let's learn various data structures and algorithms and develop the ability to implement them through programming!

Let's develop basic field knowledge of clothing science and the capabilities of a fashion specialist through understanding AI-based textile CAD!

Experience developing applications collaboratively in a Linux shell & open source software environment, and build the development environment required for program coding!

Let's understand data analysis suitable for advanced retail business and learn how to utilize tools utilized in the industry!

Let's develop the skills to develop web applications using React and develop the capabilities to apply them in practice!

Learn basic website building methods and gain practical application skills from the basics of web development through practical projects linked to public data Open API!

Let's understand and try policy evaluation with R and R Studio!

With Python Quant Investment, you can create investment strategies based on data and invest according to the strategy. You can implement various asset allocation strategies and ultimately create your own investment strategy.

This is a lecture on data visualization using the Matplotlib library.

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

This lecture is a follow-up lecture to 'Python Data-Based Stock Quant Investment Part 1'. If Part 1 was more of an introduction, Part 2 is an in-depth lecture that focuses on the entire flow of practical strategy implementation and quantitative investment development. This class focuses on advanced Pandas techniques for handling time series data, and how to implement signal-based strategies and static/dynamic asset allocation strategies that require adjusting asset weights at various intervals based on this. Furthermore, it goes beyond strategy implementation and learns about 'code framework' that directly verifies and backtests various investment strategies with minimal code modifications, how to extend this to improve it so that it can lead to actual investment, and what to watch out for in this process. In addition to the programming component, you can experience the best Python quant investment flow that you cannot find in investment books, blogs, YouTube, etc. by deeply covering theoretical contents such as the two types of return concepts (simple return, log return) and evaluation indicators related to backtesting.

You can use Python to analyze large amounts of data and visualize the analyzed data in graph form so that you can easily understand it at a glance.

Hello! Have you ever gotten tired of repeatedly writing the same code over and over again while doing data analysis? 😫 Now let's give your projects wings with PyCaret! 🚀 This course will teach you the secret to automating most of the tedious and complex machine learning processes with just 5 lines of code. Here's how this course will transform you! ✨ Master Real-World Projects: 📈Customer purchase prediction (classification), 📉Medical cost prediction (regression), 🧩Customer grouping (clustering), ⏳Airline passenger prediction (time series)! You can develop 'real' problem-solving skills by following along with 4 core projects hands-on. Acquire Expert Skills: But that's not all! 🤖 We'll also teach you expert skills like managing your models with MLOps and creating API servers with just a few clicks. Pretty amazing, right? Protect Your Work-Life Balance: Most importantly, we'll drastically reduce your repetitive tasks and help you focus only on core analysis. Leaving work on time is a bonus! 😉 Whether you're not yet comfortable with coding or you're a current professional wanting to explosively boost productivity, everyone is welcome! 👋 Shall we transform together into a '10x faster data scientist'?

In this course, you can learn a wide range of topics, from basic Excel functions to advanced formulas, data visualization, and dashboard creation, with a focus on hands-on practice. By providing templates and solution files, self-study is possible, and you can maximize work efficiency by mastering data analysis skills that can be immediately applied in real-world scenarios.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.

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.

Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

The Intermediate Python course is designed to deepen your understanding of Python by covering advanced topics such as functional programming, metaprogramming, exception handling, and object-oriented programming.

Let's develop specialized skills in programming skills, algorithm skills, and statistical analysis skills!

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.

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.

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.

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.

Finally, a probability and statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice.