Machine Learning and Data Analytics for Solving Business Problems : Methods, Applications, and Case Studies (Unsupervised and Semi-supervised Learning) / Alyoubi, Bader (EDT)/ Ben Ncir, Chiheb-Eddine (EDT)/ Alharbi, Ibraheem (EDT)

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Machine Learning and Data Analytics for Solving Business Problems : Methods, Applications, and Case Studies (Unsupervised and Semi-supervised Learning)

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Machine Learning and Data Analytics for Solving Business Problems : Methods, Applications, and Case Studies (Unsupervised and Semi-supervised Learning)

Alyoubi, Bader (EDT)/ Ben Ncir, Chiheb-Eddine (EDT)/ Alharbi, Ibraheem (EDT)

ウェブストア価格 ¥37,252（本体¥33,866）
Springer International Publishing AG（2023/12発売）
外貨定価 US$ 179.99
ポイント 338pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 206 p.
商品コード 9783031184857

Full Description

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods - such as blockchain, big data, artificial intelligence, and cloud computing - so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.

Introduction.- Supervised and unsupervised methods for customer segmentation.- Supervised and unsupervised methods for supply chain management.- Supervised and unsupervised methods for logistics improvement.- Design of recommender systems.- Supervised and unsupervised methods for e-marketing.- Analysis of Blockchain data.- Supervised and unsupervised methods applied in banking.- Cryptocurrency analysis.- Supervised and unsupervised methods to improve operational processes.- Big data analysis and summarization.- Intelligent Financial analysis and sales forecasting.- Financial data modeling and decision making.- Integration of learning methods in Smart ERP systems.- E-commerce recommender systems.- Social media and E-business analysis.- Intelligent Business control and monitoring systems.- Conclusion.