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Let's use the Folium library and Google Cloud API provided by default in Python. You can perform spatial data analysis based on coding without a separate tool.

In the [Beginner's] Data Visualization with Power BI course, you will learn how to edit data using the query editor, visualize data using various tools, and check and share the analyzed results.

This is a lecture on the National Technical Qualification Big Data Analysis Technician Practical with Python. We hope you all pass!

Analyze in an environment like Excel and automatically create Python code. This lecture will help you understand data analysis by comparing it to Pandas and Seaborn.

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

In the [Beginner's] Data Visualization with QGIS course, you will learn about basic spatial analysis methods using the QGIS program and spatial data.

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

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

This is a comprehensive lecture on machine learning. It consists of short and easy-to-understand video lectures based on practical experience.

Covers linear algebra, which is essential for machine learning/deep learning research.

Learn machine learning concepts, artificial intelligence learning principles, and mathematical principles for model implementation to fully utilize deep learning!

Still managing ML experiments manually? 🤔 Automate everything from experiment tracking to model deployment with MLflow and dramatically boost development productivity! A must-have skill for data scientists and ML engineers 💡 Start now! 🚀

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.

Learn about job analysis related to Data Science (DS) and Artificial Intelligence (AI) and the learning and employment roadmap for building a career in related jobs.

Let's develop the ability to extract useful information from large amounts of data and support decision making!

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.

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.

The "Pytorch Projects Explained in One Code" edition implements practical deep learning projects using image, video, and natural language data. It focuses on code-centered learning rather than theory, to understand the structure and flow of models.

Create AI data analysis and modeling results into a web program.

We will learn the core technologies of the 4th industrial revolution, such as ICBM (IoT, Cloud, Big Data, ML) and related technologies such as self-driving cars and metaverse, from the basics to develop the ability to prepare for future technological changes.

Ace the Big Data Analysis Engineer certification, a must-have credential that's far from easy to self-study, in a short amount of time by mastering the data analysis tool R, known for its ease of use!