data is the new oil and machine learning is powerful concept and framework for making the best out of it in this age of automation and intelligent systemsit is hardly surprise that machine learning and data science are some of the top buzz words the tremendous interest and renewed investments in the field of data science across industriesenterprisesand domains are clear indicators of its enormous potential intelligent systems and data-driven organizations are becoming reality and the advancements in tools and techniques is only helping it expand further with data being of paramount importancethere has never been higher demand for machine learning and data science practitioners than there is now indeedthe world is facing shortage of data scientists it' been coined "the sexiest job in the st centurywhich makes it all the more worthwhile to try to build some valuable expertise in this domain practical machine learning with python is problem solver' guide to building real-world intelligent systems it follows comprehensive three-tiered approach packed with conceptsmethodologieshands-on examplesand code this book helps its readers master the essential skills needed to recognize and solve complex problems with machine learning and deep learning by following data-driven mindset using real-world case studies that leverage the popular python machine learning ecosystemthis book is your perfect companion for learning the art and science of machine learning to become successful practitioner the conceptstechniquestoolsframeworksand methodologies used in this book will teach you how to thinkdesignbuildand execute machine learning systems and projects successfully this book will get you started on the ways to leverage the python machine learning ecosystem with its diverse set of frameworks and libraries the three-tiered approach of this book starts by focusing on building strong foundation around the basics of machine learning and relevant tools and frameworksthe next part emphasizes the core processes around building machine learning pipelinesand the final part leverages this knowledge on solving some real-world case studies from diverse domainsincluding retailtransportationmoviesmusiccomputer visionartand finance we also cover wide range of machine learning modelsincluding regressionclassificationforecastingrule-miningand clustering this book also touches on cutting edge methodologies and research from the field of deep learningincluding concepts like transfer learning and case studies relevant to computer visionincluding image classification and neural style transfer each consists of detailed concepts with complete hands-on examplescodeand detailed discussions the main intent of this book is to give wide range of readers--including it professionalsanalystsdevelopersdata scientistsengineersand graduate students-- structured approach to gaining essential skills pertaining to machine learning and enough knowledge about leveraging state-of-the-art machine learning techniques and frameworks so that they can start solving their own real-world problems this book is application-focusedso it' not replacement for gaining deep conceptual and theoretical knowledge about machine learning algorithmsmethodsand their internal implementations we strongly recommend you supplement the practical knowledge gained through this book with some standard books on data miningstatistical analysisand theoretical aspects of machine learning algorithms and methods to gain deeper insights into the world of machine learning xxv