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Springer Machine Learning Models and Big Data Classification

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Description

Master the complexities of big data classification with this comprehensive guide from Springer. Part of the Integrated Series in Information Systems, this book provides a clear pathway for students and newcomers to understand essential machine learning models and algorithms. By focusing on practical application, the text explores effective techniques such as decision trees for hierarchical approaches, random forests for ensemble methods, and deep learning for layered architectures. These methods are specifically selected for their ability to handle large-scale data problems efficiently. To bridge the gap between theory and practice, the book includes concrete examples and programming implementations using Matlab and R. This approach allows readers to move beyond abstract concepts and see how these technologies function in real-world scenarios. Whether you are starting your journey in information systems or looking to strengthen your technical foundation, this book offers the tools needed to grasp modern classification technologies.

Key Features

Covers essential classification models including decision trees, random forests, and deep learning architectures.

Designed specifically for students and newcomers seeking a quick understanding of big data technologies.

Provides practical programming implementations using both Matlab and R to support hands-on learning.

Explains hierarchical and layered approaches to help readers choose the right algorithm for their data.

Integrates theory with real-world examples to make complex machine learning concepts easier to grasp.

Product Specifications

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

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