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Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R (2nd Edition)
dc.contributor.author | Shmueli, Galit | |
dc.contributor.author | Gedeck, Peter | |
dc.contributor.author | C. Bruce, Peter | |
dc.contributor.author | Yahav, Inbal | |
dc.contributor.author | R. Patel, Nitin | |
dc.date.accessioned | 2024-07-16T07:21:00Z | |
dc.date.available | 2024-07-16T07:21:00Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 978-1-119-83517-2 | |
dc.identifier.uri | https://thuvienso.hoasen.edu.vn/handle/123456789/15479 | |
dc.description | 688 pages | vi |
dc.description.abstract | Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. | vi |
dc.language.iso | en | vi |
dc.publisher | Wiley | vi |
dc.subject | Machine Learning | vi |
dc.subject | Business Analytics | vi |
dc.title | Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R (2nd Edition) | vi |
dc.type | Book | vi |