now haven' told the machine learning algorithmahead of timewhat categories certain objects belong to don' have cheat sheet that it can learn from where have set of existing objects and my correct categorization of it the machine learning algorithm must infer those categories on its own this is an example of unsupervised learningwhere don' have set of answers that ' getting it learn from ' just trying to let the algorithm gather its own answers based on the data presented to it alone the problem with this is that we don' necessarily know what the algorithm will come up withif gave it that bunch of objects shown in the preceding imageis it going to group things into things that are roundthings that are large versus smallthings that are red versus bluei don' know it' going to depend on the metric that give it for similarity between items primarily but sometimes you'll find clusters that are surprisingand emerged that you didn' expect to see so that' really the point of unsupervised learningif you don' know what you're looking forit can be powerful tool for discovering classifications that you didn' even know were there we call this latent variable some property of your data that you didn' even know was there originallycan be teased out by unsupervised learning let' take another example around unsupervised learning say was clustering people instead of balls and dice ' writing dating site and want to see what sorts of people tend to cluster together there are some attributes that people tend to cluster aroundwhich decide whether they tend to like each other and date each other for example now you might find that the clusters that emerge don' conform to your predisposed stereotypes maybe it' not about college students versus middle-aged peopleor people who are divorced and whatnotor their religious beliefs maybe if you look at the clusters that actually emerged from that analysisyou'll learn something new about your users and actually figure out that there' something more important than any of those existing features of your people that really count towardto decide whether they like each other so that' an example of supervised learning providing useful results another example could be clustering movies based on their properties if you were to run clustering on set of movies from like imdb or somethingmaybe the results would surprise you perhaps it' not just about the genre of the movie maybe there are other propertieslike the age of the movie or the running length or what country it was released inthat are more important you just never know or we could analyze the text of product descriptions and try to find the terms that carry the most meaning for certain category againwe might not necessarily know ahead of time what termsor what wordsare most indicative of product being in certain categorybut through unsupervised learningwe can tease out that latent information