you could argue that it is fortunate coincidence that you are holding this book in your hands (or your -book readerafter allthere are millions of books printed every yearwhich are read by millions of readersand then there is this book read by you you could also argue that couple of machine learning algorithms played their role in leading you to this book (or this book to youand wethe authorsare happy that you want to understand more about the how and why most of this book will cover the how how should the data be processed so that machine learning algorithms can make the most out of ithow should you choose the right algorithm for problem at handoccasionallywe will also cover the why why is it important to measure correctlywhy does one algorithm outperform another one in given scenariowe know that there is much more to learn to be an expert in the field after allwe only covered some of the "howsand just tiny fraction of the "whysbut at the endwe hope that this mixture will help you to get up and running as quickly as possible what this book covers getting started with python machine learningintroduces the basic idea of machine learning with very simple example despite its simplicityit will challenge us with the risk of overfitting learning how to classify with real-world examplesexplains the use of real data to learn about classificationwhereby we train computer to be able to distinguish between different classes of flowers clustering finding related postsexplains how powerful the bag-of-words approach is when we apply it to finding similar posts without really understanding them