unless the car can learn and adapt on its own based on its experience we could also use machine learning systems as an exploration or data discovery tool to gain deeper insight into the problem we are trying to solve for examplein the email spam filter examplewe can learn which words or phrases are most predictive of spam and recognize newly emerging malicious spam patterns supervised vs unsupervised the field of machine learning has two major branches--supervised learning and unsupervised learning--and plenty of sub-branches that bridge the two in supervised learningthe ai agent has access to labelswhich it can use to improve its performance on some task in the email spam filter problemwe have dataset of emails with all the text within each and every email we also know which of these emails are spam or not (the so-called labelsthese labels are very valuable in helping the supervised learning ai separate the spam emails from the rest in unsupervised learninglabels are not available thereforethe task of the ai agent is not well-definedand performance cannot be so clearly measured consider the email spam filter problem--this time without labels nowthe ai agent will attempt to understand the underlying structure of emailsseparating the database of emails into different groups such that emails within group are similar to each other but different from emails in other groups this unsupervised learning problem is less clearly defined than the supervised learning problem and harder for the ai agent to solve butif handled wellthe solution is more powerful here' whythe unsupervised learning ai may find several groups that it later tags as being "spam"--but the ai may also find groups that it later tags as being "importantor categorize as "family,"professional,"news,"shopping,etc in other wordsbecause the problem does not have strictly defined taskthe ai agent may find interesting patterns above and beyond what we initially were looking for moreoverthis unsupervised system is better than the supervised system at finding new patterns in future datamaking the unsupervised solution more nimble on goforward basis this is the power of unsupervised learning the strengths and weaknesses of supervised learning supervised learning excels at optimizing performance in well-defined tasks with plenty of labels for exampleconsider very large dataset of images of objectswhere each image is labeled if the dataset is sufficiently large enough and we train using the right machine learning algorithms ( convolutional neural networksand with supervised vs unsupervised