Data Science

Introduction to Clustering and PCA


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About this course

Clustering and Principal Component Analysis (PCA) are very important aspects of Machine Learning and Data Science that help solve a lot of problems in a simple fashion. Clustering is widely used in a lot of applications but it finds its most popular application in grouping tasks and data compression. PCA is very much needed today as it is used to dimensionally reduce the dataset to simple datasets which are used for analysis and machine learning. In this course, you will be introduced to the concepts of clustering and PCA where you will learn about what it is, how it works, where it is popularly used and more.


  • Clustering
  • Principal Component Analysis (PCA)

Course Syllabus

Introduction to Clustering and PCA

  • Introduction to Unsupervised Learning and Clustering
  • Principal Component Analysis
  • PCA for Dimensionality Reduction

Course Certificate

Get Introduction to Clustering and PCA course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

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