文件名称:
Machine-Learning-in-Python-Essential-Techniques-for-Predictive-Analysis
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上传时间: 2018-05-16
详细说明： This book covers two broad classes of machine learning algorithms: penalized linear regression (for example, Ridge and Lasso) and ensemble methods (for example, Random Forests and Gradient Boosting). Each of these families contains variants that will solve regression and classifcation problems. (You learn the distinction between classifcation and regression early in the book.) Readers who are already familiar with machine learning and are only interested in picking up one or the other of these can skip to the two chapters coveri ng that family. Each method gets two chapters—one covering principles of operation and the other running through usage on different types of problems. Penalized linear regression is covered in Chapter 4, “Penalized Linear Regression,” and Chapter 5, “Building Predictive Models Using Penalized Linear Methods.” Ensemble methods are covered in Chapter 6, “Ensemble Methods,” and Chapter 7, “Building Predictive Models with Python.” To familiarize yourself with the problems addressed in the chapters on usage of the algorithms, you might fnd it helpful to skim Chapter 2, “Understand the Problem by Understanding the Data,” which deals with data exploration. Readers who are just starting out with machine learning and want to go through from start to finish might want to save Chapter 2 until they start looking at the solutions to problems in later chapters. ...展开收缩
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