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机器学习经典书籍,不管是学习还是工作都有很大帮助,本书是原版的PDF,内容清晰,可以复制,不是影印版,也不是其他格式转换的,很难得的高质量资源。 When teaching a technical topic, it is important to show the application of the concepts discussed to real-life problems. For this reason, we present machine learning within the context of predictive data analytics, an important and growing industry application of machine learning. The link between machine learning and data analytics runs through every chapter in the book. In Chapter 1 we introduce machine learning and explain the role it has within a standard data analytics project lifecycle. In Chapter 2 we provide a framework for designing and constructing a predictive analytics solution, based on machine learning, that meets a business need. All machine-learning algorithms assume a dataset is available for training, and in Chapter 3 we explain how to design, construct, and quality check a dataset before using it to a build prediction model. Chapters 4, 5, 6, and 7 are the main machine learning chapters in the book. Each of these chapters presents a different approach to machine learning: Chapter 4, learning through information gathering; Chapter 5, learning through analogy; Chapter 6, learning by predicting probable outcomes; and Chapter 7, learning by searching for solutions that minimize error. All of these chapters follow the same two part structure Part 1 presents an informal introduction to the material presented in the chapter, followed by a detailed explanation of the fundamental technical concepts required to understand the material, and then a standard machine learning algorithm used in that learning approach is presented, along with a detailed worked example. Part 2 of each chapter explains different ways that the standard algorithm can be extended and well-known variations on the algorithm. The motivation for structuring these technical chapters in two parts is that it provides a natural break in the chapter material. As a result, a topic can be included in a course by just covering Part 1 of a chapter (‘Big Idea’, fundamentals, standard algorithm and worked example); and then—time permitting—the coverage of the topic can be extended to some or all of the material in Part 2. Chapter 8 explains how to evaluate the performance of prediction models, and presents a range of different evaluation metrics. This chapter also adopts the two part structure of standard approach followed by extensions and variations. Throughout these technical chapters the link to the broader predictive analytics context is maintained through detailed and complete real-world examples, along with references to the datasets and/or papers that the examples are based on. The link between the broader business context and machine learning is most clearly seen in the case studies presented in Chapters 9 (predicting customer churn) and 10 (galaxy classification). In particular, these case studies highlight how a range of issues and tasks beyond model building—such as business understanding, problem definition, data gathering and preparation, and communication of insight—are crucial to the success of a predictive analytics project. Finally, Chapter 11 discusses a range of fundamental topics in machine learning and also highlights that the selection of an appropriate machine learning approach for a given task involves factors beyond model accuracy—we must also match the characteristics of the model to the needs of the business.
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