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Springer Machine Learning Models & Algorithms for Big Data Classification

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Description

This comprehensive guide from Springer presents machine learning models and algorithms specifically designed to tackle big data classification challenges. The book covers essential techniques including decision tree hierarchical approaches, random forest ensemble methods, and deep learning layered architectures that are ideally suited for handling large-scale data problems. Written with students and newcomers to the field in mind, it combines theoretical foundations with practical examples and working programs in both Matlab and R, enabling readers to quickly grasp complex concepts and apply them to real-world big data scenarios. Whether you're entering the machine learning field or seeking to deepen your understanding of classification methods, this structured learning resource bridges the gap between abstract concepts and practical implementation.

Key Features

Springer publication covering machine learning models and algorithms designed specifically for big data classification problems.

Decision tree hierarchical approach explained with theory and examples to simplify complex classification tasks.

Random forest ensemble methods presented with practical implementations for improved prediction accuracy.

Deep learning layered approach included with Matlab and R code examples for hands-on learning.

Designed for students and newcomers seeking quick understanding of machine learning techniques and technologies.

Practical programs in Matlab and R enable readers to implement and test algorithms on actual big data problems.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
23 August 2016
Listed Since
14 May 2016

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