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About This BookGet to grips with the application of Machine Learning methods using an extensive set of R packagesUnderstand the benefits and potential pitfalls of using machine learning methodsImplement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML systemWho This Book Is ForIf you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistics or machine learning will prove helpful.What You Will LearnGain deep insights to learn the applications of machine learning toolsManipulate data in R efficiently to prepare it for analysisMaster the skill of recognizing techniques for the effective visualization of dataUnderstand why and how to create test and training datasets for analysisFamiliarize yourself with fundamental learning methods such as linear and logistic regressionMaster advanced learning methods such as support vector machinesRealize why and how to apply unsupervised learning methodsIn Detail
Machine learning is a field of Artificial Intelligence, which aims to build systems that learn from data. Given the growing prominence of R a cross-platform, zero-cost statistical programming environment there has never been a better time to start applying machine learning to your data.
The book starts with an introduction to the cross-industry standard process for data mining. It also takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of "Unsupervised techniques." Finally, the book will walk you through text analysis and time series.
The book provides you with practical and real-world solutions to problems and teaches you a variety of tasks, such as building complex recommendation systems. By the end of this book, you will have gained expertise in building complex ML projects using R and its packages."
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