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

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Science of Information,verview focuses on two important paradigms: (1) big data paradigm, which describes a problem space for the big data analytics, and (2) machine learning paradigm, which describes a solution space for the big data analytics. It also includes a preliminary description of the important elements of data

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Big Data Analytics objective of this chapter is to illustrate some of the meaningful changes that may occur in a set of data when it is transformed into big data through evolution. To make this objective practical and interesting, a split-merge-split frameworkis developed, presented, and applied in this chapter. A se

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Distributed File Systemfication problem. This system can help one to implement, test, and evaluate various machine-learning techniques presented in this book for learning purposes. The objectives include a detailed explanation of the Hadoop framework and the Hadoop system, the presentation of the Internet resources that c

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MapReduce Programming Platformlies on its underlying structures, the parametrization, and the parallelization. These structures have been explained clearly in this chapter. The implementation of these structures requires a MapReduce programming platform. An explanation of this programming platform is also presented together with

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Modeling and Algorithmsd supervised learning (regression and classification) and unsupervised learning (clustering) using examples. Modeling and algorithms will be explained based on the domain division characteristics, batch learning and online learning will be explained based on the availability of the data domain, and

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Supervised Learning Modelsping that projects a data domain into a response set, and thus helps extract knowledge (known) from data (unknown). These learning models, in simple form, can be grouped into predictive models and classification models. Firstly, the predictive models, such as the standard regression, ridge regressio

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Supervised Learning Algorithmsthe supervised learning algorithms support the search for optimal values for the model parameters by using large data sets without overfitting the model. Therefore, a careful design of the learning algorithms with systematic approaches is essential. The machine learning field suggests three phases f

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Support Vector Machinecan help the multidomain applications in a big data environment. However, the support vector machine is mathematically complex and computationally expensive. The main objective of this chapter is to simplify this approach using process diagrams and data flow diagrams to help readers understand theor

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Decision Tree Learning tree. It has two categories: classification tree and regression tree. The theory and applications of these decision trees are explained in this chapter. These techniques require tree split algorithms to build the decision trees and require quantitative measures to build an efficient tree via traini