Introduction to Clustering and PCA

The principal component analysis is a statistical technique used to reduce the dimensionality of a data set. Clustering is a method used to group data points into clusters.

Some of the reasons for the popularity of PCA and clustering include:

• They are both relatively simple to implement and understand, meaning that practitioners with limited statistical knowledge can use them.
• They are powerful methods used to extract meaningful information from data.
• They can be used together to provide even more insights into the data.

Some of the jobs that demand you learn principal component analysis and clustering include:

• Data Analyst
• Statistician
• Research Scientist
• Machine Learning Engineer
• Data Scientist

Yes. Complete all the modules suggested and the quiz at the end of the course to receive a free course completion certificate.

By the end of this free clustering and PCA course, you will have better knowledge of clustering and principal component analysis and their impact and applications.

Introduction to Clustering and PCA is a free course offered by Great Learning and can be attained by any learner.

No, there is no limit on the number of times you attain this course.

Yes, you can sign up for multiple free courses offered by Great Learning Academy that aid to reach your career goals sooner.

Any learner enthusiastic about exploring Data Science and Machine Learning techniques can enroll in this course to get familiar with the in-demand methods.

• Search for the free course “Introduction to Clustering and PCA” in the search bar present at the top corner of Great Learning Academy.
• Register for the course through the Enroll Now button and start learning.