[machine_learning_mastery系列]Master_Machine_Learning_Algorithms.pdf

Preface Machine learning algorithms dominate applied machine learning. Because algorithms are such a big part of machine learning you must spend time to get familiar with them and really understand how they work. I wrote this book to help you start this journey. You can describe machine learning algorithms using statistics, probability and linear algebra. The mathematical descriptions are very precise and often unambiguous. But this is not the only way to describe machine learning algorithms. Writing this book, I set out to desc ribe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in. ribe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in.