machine learning and train/test so what is machine learningwellif you look it up on wikipedia or whateverit'll say that it is algorithms that can learn from observational data and can make predictions based on it it sounds really fancyrightlike artificial intelligence stufflike you have throbbing brain inside of your computer but in realitythese techniques are usually very simple we've already looked at regressionswhere we took set of observational datawe fitted line to itand we used that line to make predictions so by our new definitionthat was machine learningand your brain works that way too another fundamental concept in machine learning is something called train/testwhich lets us very cleverly evaluate how good machine learning model we've made as we look now at unsupervised and supervised learningyou'll see why train/test is so important to machine learning unsupervised learning let' talk in detail now about two different types of machine learningsupervised and unsupervised learning sometimes there can be kind of blurry line between the twobut the basic definition of unsupervised learning is that you're not giving your model any answers to learn from you're just presenting it with group of data and your machine learning algorithm tries to make sense out of it given no additional informationlet' say give it bunch of different objectslike these balls and cubes and sets of dice and what not let' then say have some algorithm that will cluster these objects into things that are similar to each other based on some similarity metric