introduction machine learning is about extracting knowledge from data it is research field at the intersection of statisticsartificial intelligenceand computer science and is also known as predictive analytics or statistical learning the application of machine learning methods has in recent years become ubiquitous in everyday life from automatic recommendations of which movies to watchto what food to order or which products to buyto personalized online radio and recognizing your friends in your photosmany modern websites and devices have machine learning algorithms at their core when you look at complex website like facebookamazonor netflixit is very likely that every part of the site contains multiple machine learning models outside of commercial applicationsmachine learning has had tremendous influence on the way data-driven research is done today the tools introduced in this book have been applied to diverse scientific problems such as understanding starsfinding distant planetsdiscovering new particlesanalyzing dna sequencesand providing personalized cancer treatments your application doesn' need to be as large-scale or world-changing as these examples in order to benefit from machine learningthough in this we will explain why machine learning has become so popular and discuss what kinds of problems can be solved using machine learning thenwe will show you how to build your first machine learning modelintroducing important concepts along the way why machine learningin the early days of "intelligentapplicationsmany systems used handcoded rules of "if and "elsedecisions to process data or adjust to user input think of spam filter whose job is to move the appropriate incoming email messages to spam folder you could make up blacklist of words that would result in an email being marked as