Jae-sung Yoon's Python Data Analysis for Machine Learning/Deep Learning (Ai part1)

This course is a Java Android app development course consisting of a total of 4 parts. In the second lecture, we will look at the menu and the 4 major components through practical examples.

This course covers everything from JSP basics to a car purchasing shopping mall project. After completing the course, you will be able to carry out a real project right away. (This course does not provide Q&A.)

Easy explanations and examples are provided so that you can apply them directly to your work.

Currently, Android development can be done using both Java and Kotlin. You can develop the same Android applications regardless of which language you choose. This course covers most aspects of Android application development, from basics to advanced topics, and all code is written using Kotlin starting from the fundamentals. Additionally, it utilizes Google's latest UI library, Material 3. This course involves completing three projects throughout the entire Android curriculum. By working on these projects, you can master advanced techniques actually used in the industry, such as Hilt, Retrofit, Room Database, and MVVM. Furthermore, you can increase productivity using AI by performing "vibe coding" with Gemini within the Android development tools. Soft Campus will always strive to provide the most up-to-date lectures.

CSS & CSS3 will be the easiest to learn. Good luck!!

You can understand JSP WEB MVC Model2 Programming and learn how to apply it in practice through the MVC Model2 bulletin board project. (This lecture does not provide Q&A with the instructor.)

Android can be developed using Java and Kotlin languages. You can develop the same Android application using either language. This course covers most content from basics to advanced topics in Android application development, and all code is written using the Kotlin language from the fundamental content onwards. It also utilizes Material 3, Google's latest UI library. This course primarily focuses on action bars, data management, resource management, sensors, and networking within the overall Android development curriculum.

Currently, Android can be developed using the Java and Kotlin languages. The same Android application can be developed using either language. This course covers most of the content from basic to advanced Android application development, and all code is written using the Kotlin language starting from the basic content. We also use Material 3, Google's latest UI library. This course mainly covers menus, Android application components, threads, and Fragments among the entire Android curriculum, and future courses are planned to cover more in-depth topics. Soft Campus will always strive to provide the latest lectures. Thank you.

You can create hybrid casual games using Unity 6.

Learn C# in the Unity environment. Learning C# will help you naturally learn the Unity development environment, which will be very beneficial for beginner developers.

Android 15 Version App Development with Latest Material 3 Library Part 1 UI Programming Course.

With the latest version in 2024 and a running time of 17 hours and 48 minutes, you can completely conquer Kotlin with this one course.

This course teaches you how to develop front-end web using React. You will learn the core JavaScript grammar for React, CDN-based React development, and NPM-based React development methods.

Using Unity based on the C# language, you will learn how to create a defense game step by step.

Develop three practical projects (memo application, LBS application, bulletin board application using FireBase).

You can learn the basic principles of run games like Cookie Run and create Unity games through practical projects in Unity by genre.

This course is a Java Android app development course consisting of a total of 4 parts. In the third lecture, we will look at data management and networks in Android apps.

This lecture is a lecture by instructor Jaeseong Yoon, who teaches Android lectures at App School: Like a Cool Lion.

This course is a course to learn the basics of Python Windows programming. It enhances understanding by explaining how to use widgets and providing sample codes.

This course is a course to learn the basics of Python grammar. It enhances understanding by combining grammar explanations with sample code. Learn basic Python libraries.

This is a course for beginners to master the fundamental techniques of the entire Python data analysis process. As an e-commerce planner and developer currently applying data analysis techniques in the field, I have designed this course to help you easily learn and immediately apply the entire Python data analysis workflow.

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.

Learn how to work efficiently using Python, a basic programming language.

We guide non-majors and beginners to quickly obtain the Big Data Analytics Engineer practical certification! Theory lightly, practice thoroughly - even without complex background knowledge, we focus on learning only the essential points that appear on the exam, centered around past exam questions.

SSIS, SSAS, MachineLearning, BI, ETL. You can learn important technologies that cannot be found in domestic books, YouTube, lectures, blogs, and academies. I also recommend it to those who are interested in employment at domestic large companies, American large companies, and American state funding agencies.

From the basics of Numpy and Pandas to data preprocessing, visualization, and crawling, all in one place! This is an introductory Python course for data analysis.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.

This course is designed to help beginners understand artificial intelligence in a simple, practical, and structured way. Many students feel overwhelmed by AI because of complex terminology, unclear explanations, or a lack of real examples. In this course, I focus on breaking down AI concepts into easy-to-understand ideas, using visual tools, real-world examples, and step-by-step demonstrations. Instead of heavy mathematics or theory, you will learn how AI works, how to use popular AI tools, and how to apply AI confidently for learning, productivity, development, and business use cases. By the end of the course, you will have a solid foundation in AI, understand modern AI tools, and feel comfortable exploring more advanced topics.

If you want to be recognized as a machine learning expert based on large-scale data, from understanding the core framework of Spark machine learning, to SQL-based data processing through difficult practical problems, to data analysis through business domain analysis, and to the ability to implement optimized machine learning models, please join this course.

You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.

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.

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

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

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.

To keep up with recent deep learning trends, we examine the context of deep learning's groundbreaking advancements.

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.

ADsP Certification All-In-One Course! Ultra-Short Completion! Reference Book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

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.