Unsupervised Learning: Clustering

Introduction

In this course, you will fully understand unsupervised learning and learn to use unsupervised learning to perform data clustering.

🎯 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

🏆 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