given set of customer recordsyou might want to identify which customers are similarand whether there are groups of customers with similar preferences for shopping sitethese might be "parents,"bookworms,or "gamers because you don' know in advance what these groups might beor even how many there areyou have no known outputs detecting abnormal access patterns to website to identify abuse or bugsit is often helpful to find access patterns that are different from the norm each abnormal pattern might be very differentand you might not have any recorded instances of abnormal behavior because in this example you only observe trafficand you don' know what constitutes normal and abnormal behaviorthis is an unsupervised problem for both supervised and unsupervised learning tasksit is important to have representation of your input data that computer can understand often it is helpful to think of your data as table each data point that you want to reason about (each emaileach customereach transactionis rowand each property that describes that data point (saythe age of customer or the amount or location of transactionis column you might describe users by their agetheir genderwhen they created an accountand how often they have bought from your online shop you might describe the image of tumor by the grayscale values of each pixelor maybe by using the sizeshapeand color of the tumor each entity or row here is known as sample (or data pointin machine learningwhile the columns--the properties that describe these entities--are called features later in this book we will go into more detail on the topic of building good representation of your datawhich is called feature extraction or feature engineering you should keep in mindhoweverthat no machine learning algorithm will be able to make prediction on data for which it has no information for exampleif the only feature that you have for patient is their last nameno algorithm will be able to predict their gender this information is simply not contained in your data if you add another feature that contains the patient' first nameyou will have much better luckas it is often possible to tell the gender by person' first name knowing your task and knowing your data quite possibly the most important part in the machine learning process is understanding the data you are working with and how it relates to the task you want to solve it will not be effective to randomly choose an algorithm and throw your data at it it is necessary to understand what is going on in your dataset before you begin building model each algorithm is different in terms of what kind of data and what problem setting it works best for while you are building machine learning solutionyou should answeror at least keep in mindthe following questions introduction