machine learning is an integral part of many commercial applications and research projects todayin areas ranging from medical diagnosis and treatment to finding your friends on social networks many people think that machine learning can only be applied by large companies with extensive research teams in this bookwe want to show you how easy it can be to build machine learning solutions yourselfand how to best go about it with the knowledge in this bookyou can build your own system for finding out how people feel on twitteror making predictions about global warming the applications of machine learning are endless andwith the amount of data available todaymostly limited by your imagination who should read this book this book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems this is an introductory book requiring no previous knowledge of machine learning or artificial intelligence (aiwe focus on using python and the scikit-learn libraryand work through all the steps to create successful machine learning application the methods we introduce will be helpful for scientists and researchersas well as data scientists working on commercial applications you will get the most out of the book if you are somewhat familiar with python and the numpy and matplotlib libraries we made conscious effort not to focus too much on the mathbut rather on the practical aspects of using machine learning algorithms as mathematics (probability theoryin particularis the foundation upon which machine learning is builtwe won' go into the analysis of the algorithms in great detail if you are interested in the mathematics of machine learning algorithmswe recommend the book the elements of statistical learning (springerby trevor hastierobert tibshiraniand jerome friedmanwhich is available for free at the authorswebsite we will also not describe how to write machine learning algorithms from scratchand will instead focus on vii