Foundations of Clustering in Machine Learning

Unsupervised learning can seem challenging when you do not have labeled data to guide your models. Clustering solves this by finding hidden patterns and grouping similar data points automatically. This text-based course guides you from the absolute basics of clustering to confidently applying unsupervised algorithms to real-world datasets. You will learn how to structure unlabeled data, choose the right algorithms, and measure your model performance. What you'll learn: Understand the core concepts of unsupervised learning and how clustering differs from supervised classification; Implement popular clustering algorithms including K-Means, DBSCAN, and Hierarchical Clustering; Evaluate cluster quality using modern metrics like Silhouette Score and Inertia; Prepare and scale features correctly to ensure accurate distance-based grouping; Apply clustering techniques to real-world scenarios like customer segmentation; Test your understanding with built-in conceptual quizzes and practical text-based exercises. The course begins with essential terminology and foundational definitions of distance metrics. You will then progress step-by-step through algorithm mechanics, feature preprocessing, and evaluation strategies using modern data practices. This course is designed for beginners in data science and machine learning, with no prior modeling experience required. Start exploring the hidden structures in your data today.