Titlebook: Machine Learning Models and Algorithms for Big Data Classification; Thinking with Exampl Shan Suthaharan Book 2016 Springer Science+Busines

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Distributed File Systemstep-by-step instruction to build the RevolutionAnalytics’ RHadoop system for your big data computing environment. The objective also includes the presentation of simple examples to test the system to ensure the Hadoop system works. A brief discussion on setting up a multi node Hadoop system is also presented.

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Decision Tree Learninghe training algorithms suitable for classification tree and regression tree models. Simple examples and visual aids explain the difficult concepts so that readers can easily grasp the theory and applications of decision tree.

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Dimensionality Reductionf eigenvalues and eigenvectors, and these terminologies are explained in detail with examples. The principal component analysis is also explained using a simple two-dimensional example, and several coding examples are also presented.

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Book 2016decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learnin

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Shan SuthaharanAddresses a new and hot field of Big Data Science and Engineering.Offers new Machine Learning techniques and solutions.Provides solutions to overcome Big Data classification problems that industries,