Clustering in Machine Learning – Discover Hidden Patterns in Data

Welcome to Imarticus Learning! In this video, we explore Clustering, an unsupervised machine learning technique used to group data points based on similarity—without the need for labeled outcomes. You’ll gain a strong understanding of key clustering methods such as K-Means, Hierarchical Clustering, and DBSCAN, along with their real-world applications in driving smarter business decisions. Whether you're just beginning your data science journey or upskilling in machine learning, this session is designed to help you uncover hidden patterns in data and enhance your analytical mindset.

We kick off with an introduction to clustering, followed by an in-depth explanation of how K-Means clustering works—including centroids, inertia, and choosing the right 'k'. Next, we move into Hierarchical Clustering, its visual appeal through dendrograms, and wrap up with DBSCAN for density-based clustering. We also dive into model evaluation techniques like the silhouette score and elbow method to help you determine clustering effectiveness.

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