What you'll learn

Skills you'll gain

There are 6 modules in this course

By the end of this course, students will be able to: 1. Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction. 2. Grasp the concepts and applications of partitioning, hierarchical, density-based, and grid-based clustering methods. 3. Explore the mathematical foundations of clustering algorithms to comprehend their workings. 4. Apply clustering techniques to diverse datasets for pattern discovery and data exploration. 5. Comprehend the concept of dimension reduction and its importance in reducing feature space complexity. 6. Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space. 7. Evaluate clustering results and dimension reduction effectiveness using appropriate performance metrics. 8. Apply clustering and dimension reduction techniques in real-world case studies to derive meaningful insights. Throughout the course, students will actively engage in tutorials and case studies, strengthening their clustering analysis and dimension reduction skills and gaining practical experience in applying these techniques to diverse datasets. By achieving the learning objectives, participants will be well-equipped to excel in unsupervised learning tasks and make informed decisions using clustering and dimension reduction techniques.

Hierarchical Clustering

Density-based Clustering

Grid-based Clustering

Dimension Reduction Methods

Case Study

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