Monthly Subscription Starting at AED 99 + VAT

Clustering Analysis
  • Level Professional
  • Duration 37 hours
  • Course by University of Colorado Boulder
  • Offered by

About

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.

Modules

Introduction to Clustering

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Introduction to Clustering

2 Readings

  • Assessment Strategy
  • Activity Strategy

Partitioning Clustering

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Partitioning Clustering Quiz

1 Discussions

  • Partitioning Clustering Exploration Exercise

1 Videos

  • Partitioning Clustering

3 Readings

  • Partitioning Clustering Demo
  • Partitioning Clustering Case Study - Iris
  • Partitioning Clustering Case Study

Hierarchical Clustering

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Hierarchical Clustering Quiz

1 Discussions

  • Hierarchical Clustering Exploration Exercise

1 Videos

  • Hierarchical Clustering

3 Readings

  • Hierarchical Clustering Demo
  • Hierarchical Clustering Case Study - Iris
  • Hierarchical Clustering Case Study

Density-based Clustering

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Density-based Clustering Quiz

1 Discussions

  • Density-based Clustering Exploration Exercise

1 Videos

  • Density-based Clustering

3 Readings

  • Density-based Clustering Demo
  • Density-based Clustering Case Study - Iris
  • Density-based Clustering Case Study

Grid-based Clustering

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Grid-based Clustering Quiz

1 Discussions

  • Grid-based Clustering Exploration Exercise

1 Videos

  • Grid-based Clustering

2 Readings

  • Grid-based Clustering Demo
  • Grid-based Clustering - Two Moons

Dimension Reduction Methods

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Dimension Reduction Quiz

1 Discussions

  • Dimension Reduction Exploration Exercise

1 Videos

  • Dimension Reduction Methods

3 Readings

  • Dimension Reduction Demo
  • Dimension Reduction Case Study - Wines
  • Dimension Reduction Case Study

Case Study

    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Self Reflection

1 Discussions

  • Clustering Analysis Exploration Exercise

1 Readings

  • Clustering Analysis Case Study - Demo

Auto Summary

"Clustering Analysis" is a professional-level course in Data Science & AI offered by Coursera. This course delves into unsupervised learning, emphasizing clustering methods and dimension reduction techniques such as PCA. Through interactive tutorials and real-world case studies, participants will gain hands-on experience in partitioning, hierarchical, density-based, and grid-based clustering. The course spans 2220 minutes and offers a Starter subscription option, making it ideal for data science enthusiasts seeking to enhance their skills in clustering analysis and dimension reduction.

Di Wu
Instructor

Di Wu