Christo El Morr - Machine Learning for Practical Decision Making: A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics

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This book fills a gap in the machine learning literature. It presents the machine learning concepts in a very simple and practical way starting with a tangible example and building on it the theory. Each machine learning chapter starts with a presentation of the problem to solve, followed by a practical example on how the algorithm that solves the problem works, then comes the presentation of the machine learning algorithm theory and closing remarks (e.g., pitfalls, advantages limitations). This is followed by a set of key terms, a set of questions to test your understanding of the chapter material, a set of references to read more about the subject and hands-on lab exercises. The lab exercises allow readers to apply the chapters concepts and gain needed skills. To maximize the benefit for readers and to expose them to a myriad of machine learning languages and frameworks, the lab exercises (and sometimes the chapters material) are built around Python mainly, followed by R (and R Studio), and Weka. One of the strengths of this book is that it can be used by people who are exposed to programming or would like to learn how to program and those who prefer not to program but to solve decision-making problems with machine learning using simple graphical user interface. The former can use Python, the machine learning language par excellence, throughout all chapters, or R (limited to Chaps..