Machine learning is an integral part of many commercial applications and research projects today, in 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 book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data avail‐ able today, mostly 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 introduc‐ tory book requiring no previous knowledge of machine learning or artificial intelli‐ gence (AI). We focus on using Python and the scikit-learn library, and work through all the steps to create a successful machine learning application. The meth‐ ods we introduce will be helpful for scientists and researchers, as well as data scien‐ tists 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.