libraries why we wrote this book there are many books on machine learning and ai howeverall of them are meant for graduate students or phd students in computer scienceand they're full of advanced mathematics this is in stark contrast with how machine learning is being usedas commodity tool in research and commercial applications todayapplying machine learning does not require phd howeverthere are few resources out there that fully cover all the important aspects of implementing machine learning in practicewithout requiring you to take advanced math courses we hope this book will help people who want to apply machine learning without reading up on yearsworth of calculuslinear algebraand probability theory navigating this book this book is organized roughly as follows introduces the fundamental concepts of machine learning and its applicationsand describes the setup we will be using throughout the book and describe the actual machine learning algorithms that are most widely used in practiceand discuss their advantages and shortcomings discusses the importance of how we represent data that is processed by machine learningand what aspects of the data to pay attention to covers advanced methods for model evaluation and parameter tuningwith particular focus on cross-validation and grid search explains the concept of pipelines for chaining models and encapsulating your workflow shows how to apply the methods described in earlier to text dataand introduces some text-specific processing techniques offers high-level overviewand includes references to more advanced topics while and provide the actual algorithmsunderstanding all of these algorithms might not be necessary for beginner if you need to build machine learning system asapwe suggest starting with and the opening sections of which introduce all the core concepts you can then skip to "summary and outlookon page in which includes list of all the supervised models that we cover choose the model that best fits your needs and flip back to read the viii preface