University of Colorado Boulder - Clustering Analysis
- Offered byCoursera
Clustering Analysis at Coursera Overview
Duration | 37 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Clustering Analysis at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 5 quizzes, 1 assignment
Clustering Analysis at Coursera Course details
- What you'll learn
- Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.
- Apply clustering techniques to diverse datasets for pattern discovery and data exploration.
- Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.
- The "Clustering Analysis" course introduces students to the fundamental concepts of unsupervised learning, focusing on clustering and dimension reduction techniques. Participants will explore various clustering methods, including partitioning, hierarchical, density-based, and grid-based clustering. Additionally, students will learn about Principal Component Analysis (PCA) for dimension reduction. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying clustering and dimension reduction techniques to diverse datasets.
- 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.
Clustering Analysis at Coursera Curriculum
Introduction and Partitioning Clustering
Introduction to Clustering
Partitioning Clustering
Assessment Strategy
Activity Strategy
Partitioning Clustering Demo
Partitioning Clustering Case Study - Iris
Partitioning Clustering Case Study
Partitioning Clustering Quiz
Partitioning Clustering Exploration Exercise
Hierarchical Clustering
Hierarchical Clustering
Hierarchical Clustering Demo
Hierarchical Clustering Case Study - Iris
Hierarchical Clustering Case Study
Hierarchical Clustering Quiz
Hierarchical Clustering Exploration Exercise
Density-based Clustering
Density-based Clustering
Density-based Clustering Demo
Density-based Clustering Case Study - Iris
Density-based Clustering Case Study
Density-based Clustering Quiz
Density-based Clustering Exploration Exercise
Grid-based Clustering
Grid-based Clustering
Grid-based Clustering Demo
Grid-based Clustering - Two Moons
Grid-based Clustering Quiz
Grid-based Clustering Exploration Exercise
Dimension Reduction Methods
Dimension Reduction Methods
Dimension Reduction Demo
Dimension Reduction Case Study - Wines
Dimension Reduction Case Study
Dimension Reduction Quiz
Dimension Reduction Exploration Exercise
Case Study
Clustering Analysis at Coursera Admission Process
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