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

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Deep Learning Modelsnd provide programming examples that help you clearly understand these approaches. These techniques heavily depend on the stochastic gradient descent approach; and this approach is also discussed in detail with simple iterative examples. These parametrized deep learning techniques are also dependent

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Chandelier Decision Tree tree and the random forest. The chapter also presents a previously proposed algorithm called the unit circle algorithm (UCA) and proposes a family of UCA-based algorithms called the unit circle machine (UCM), unit ring algorithm (URA), and unit ring machine (URM). The unit circle algorithm integrat

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Dimensionality Reductionis, that can support scaling-up machine learning. The standard and flagged feature hashing approaches are explained in detail. The feature hashing approach suffers from the hash collision problem, and this problem is reported and discussed in detail in this chapter, too. Two collision controllers, f

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MapReduce Programming Platformprovide good programming practices to the users of the MapReduce programming platform in the context of big data processing and analysis. Several programming examples are also presented to help the reader to practice coding principles and better understand the MapReduce framework.

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Random Forest Learning chapter include detailed discussions on these approaches. The chapter also discusses the training and testing algorithms that are suitable for the random forest supervised learning. The chapter also presents simple examples and visual aids to better understand the random forest supervised learning technique.

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1571-0270 overcome Big Data classification problems that industries, .This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach)

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