코드로 한 번에 이해되는 파이토치_기본편

This lecture aims to systematically learn the essential core concepts and functions absolutely necessary for the Business Information Visualization Practical Exam (Tableau), based on the exam guidelines of the organizing institution. It is structured around practical exercises so that you can freely use the functions frequently tested in the actual exam, and through solving mock exam problems of types A and B, you can improve your understanding of the exam types and your problem-solving skills together. Lecture plan and materials can be checked at https://github.com/hansikyung/Tableau_OneTime.

The PyTorch_Project Edition, understood instantly with code, implements practical deep learning projects using image, video, and NLP data, understanding model structure and flow through code-centric learning over theory.

We'll concisely summarize only the essentials of Python syntax!! For those who want to quickly learn only the must-knows like data types, conditionals, loops, and functions; for those who wish to understand Pythonic syntax in a short time, This is recommended for office workers, job seekers, and non-majors who want to master Python in a short period.

The Business Information Visualization Capability Certification course focuses on cultivating the ability to effectively analyze and visualize data, and clearly convey information necessary for business decision-making. Through this, as talent equipped with data-driven strategic thinking and practical capabilities, you can acquire diverse knowledge and skills helpful for personal competency development and work performance improvement. This course was created for written exam candidates of the newly established 'Business Information Visualization' course in 2024. With content faithful to the exam guidelines from Subject 1 to Subject 3, we help you pass your written exam.

The PyTorch_Project Edition, understood instantly with code, implements practical deep learning projects using image, video, and NLP data, understanding model structure and flow through code-centric learning over theory.

DNN, CNN, RNN? This lecture perfectly organizes the core algorithms of deep learning and puts them into practice through practical deep learning training, even with difficult-looking technical terms!

By understanding the working principles of various recommendation algorithms, you can create your own personalized recommendation algorithm!

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

This course is designed to learn the fundamental theories and practical processes necessary for applying AI modeling and service planning to real-world work environments. Learners can acquire essential foundational knowledge to skillfully perform AI modeling projects and experience various practical procedures used in actual business settings. The course is specifically structured to provide step-by-step learning of the entire workflow from AI service environment analysis → goal setting → requirements analysis → service model design → scenario planning → utilization planning → execution plan development → performance evaluation planning. Through this approach, learners can develop systematic thinking and practical application skills for how to introduce and utilize AI from business and service planning perspectives, rather than limiting themselves to purely technical learning. Upon completing this course, learners will understand the overall planning and execution process of AI projects and will be able to strengthen their capabilities as AI service planners, PMs, and data-driven strategists in actual corporate environments.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!

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.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

Let's have fun coding with various cute robots using artificial intelligence!

By utilizing the operating principles and development tools of Microsoft 365 Copilot agents, you can systematically learn how to design and expand agents that operate in the way you desire. Chapter 1 organizes the essential concepts and principles to know before creating an agent, Chapter 2 explores agent types and tools suitable for various scenarios, and Chapter 3 allows you to directly implement your own agent using Agent Builder, SharePoint Agent, and Copilot Studio. From basics to hands-on practice, this single course will enable you to connect the potential of Copilot agents to practical applications!

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

Explains the basic principles of large language models like ChatGPT, focusing on theory.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

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 :)