Unsupervised Learning: Clustering and Dimensionality Reduction

Course Summary

This course provides a comprehensive introduction to unsupervised learning, focusing on clustering, dimensionality reduction, and anomaly detection. Participants will explore key algorithms such as K-Means, DBSCAN, Hierarchical Clustering, and Principal Component Analysis (PCA) to uncover hidden patterns in data without labeled outcomes. The course emphasizes real-world applications, including customer segmentation, anomaly detection, genetics, and image compression, using Python and Scikit-learn.

Key Learning Outcomes

Understand unsupervised learning principles and their applications.
Implement clustering techniques (K-Means, DBSCAN, Agglomerative Clustering) for pattern discovery.
Apply dimensionality reduction methods (PCA, t-SNE) for feature extraction and visualization.
Learn anomaly detection techniques for fraud detection and system monitoring.
Work on real-world datasets to extract meaningful insights from unlabeled data.

Target Audience

Data scientists, analysts, researchers, AI practitioners, and professionals handling large, unstructured datasets.

Prerequisites

Basic knowledge of Python programming and machine learning fundamentals.

Familiarity with linear algebra and probability concepts is beneficial but not required.

Course Duration & Format

4 days (Online) – Includes interactive coding exercises, practical projects, and real-world case studies.

Instructor(s)

Experienced machine learning specialists, data scientists, and AI practitioners.

Course Fee & Registration