Introduction
In this course, you will fully understand unsupervised learning and learn to use unsupervised learning to perform data clustering.
๐ฏ Tasks
In this course, you will learn:
- How to perform different types of clustering techniques, including centroid-based, hierarchical, density-based, and spectral clustering
- How to apply clustering methods to real-world problems, such as image compression and bike-sharing distribution tracking
- How to evaluate the performance of common clustering methods
๐ Achievements
After completing this course, you will be able to:
- Understand the principles and applications of unsupervised learning, particularly in the context of data clustering
- Implement and apply various clustering algorithms to solve practical problems
- Evaluate the effectiveness of different clustering methods and select the appropriate technique for a given task
- Leverage clustering techniques to gain insights from unlabeled data and support decision-making processes
