Machine Learning for Business Analytics : Concepts, Techniques and Applications in RapidMiner / Galit Shmueli [et al.]
Material type: TextLanguage: English Publication details: Hoboken : John Wiley & Sons, 2023Edition: 1st editionDescription: xxix, 699 p. ; 28 cmISBN: 9781119828792Subject(s): Machine Learning | Business Analytics | Học bằng máy | Phân tích kinh doanh | Phân tích dữ liệuDDC classification: 006.3 Online resources: Click here to access online Summary: Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner 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. This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|
Giáo trình |
Thư viện Trường Quốc tế - Cơ sở Hòa Lạc
Thư viện Trường Quốc tế - Đại học Quốc gia Hà Nội |
006.3 MAC 2023 | Available | HL.1/00740 | ||
Giáo trình |
Thư viện Trường Quốc tế - Cơ sở Trịnh Văn Bô
Thư viện Trường Quốc tế - Đại học Quốc gia Hà Nội |
006.3 MAC 2023 | Available | TVB.1/00956 |
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner 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.
This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
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