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Data Science in 90 Seconds: Principal Component Analysis

By   /  August 13, 2018  /  No Comments

Click to learn more about video blogger Laura Kahn.

This is Lesson 6 in the Data Science in 90 Seconds video blog series from host Laura Kahn. The series covers some of the most prominent questions in Data Science such as Supervised and Unsupervised Learning, K-Means Clustering, Naive Bayes, Decision Trees and Random Forests, Ridge Regression, and more.

In today’s video blog Laura discusses Principal Component Analysis.

About the author

Laura was born in a small town in North Carolina. She went on to earn a B.S. in Textile Engineering and a B.A. in Spanish at North Carolina State University. Laura thought this unique combination of majors would be amazing after attending a summer camp in high school where she played with bouncing polymers. While attending North Carolina State University, she earned a scholarship to study a summer term in Peru, where she fell in love with the Spanish language. Upon graduation, she moved to Washington, D.C. where she served in a variety of digital information roles. After over a decade in public service, she decided to change careers and pursue a Master’s of Data Science from Indiana University’s School of Informatics, Computing and Engineering. Her research includes applied artificial intelligence and has been featured at several conferences. Laura loves traveling, hosting dinner parties, playing saxophone and spending time with her family. Follow Laura at: Twitter, LinkedIn

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