Interesting 

Unsupervised Machine Learning: Crash Course Statistics #37

Today we’re going to discuss how machine learning can be used to group and label information even if those labels don’t exist. We’ll explore two types of clustering used in Unsupervised Machine Learning: k-means and Hierarchical clustering, and show how they can be used in many ways – from book suggestions and medical interventions, to giving people better deals on pizza!

Special thanks to Michele Atterson and the Butler University Student Disability Services Office for help with this video.

Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse

Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:

Sam Buck, Mark Brouwer, James Hughes, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore

Want to find Crash Course elsewhere on the internet?
Facebook – http://www.facebook.com/YouTubeCrashCourse
Twitter – http://www.twitter.com/TheCrashCourse
Tumblr – http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse

CC Kids: http://www.youtube.com/crashcoursekids

Related posts

Leave a Comment