9781394286799 - Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python di Shmueli, Galit; Bruce, Peter C.; Gedeck, Peter; Patel, Nitin R. (30 risultati)

Hardcover. Condizione: Fair. 2. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Hardcover. Condizione: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condizione: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condizione: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condizione: New.

hardcover. Condizione: New. 2nd Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: new.

hardcover. Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In.

Condizione: NEW.

Hardcover. Condizione: new. Hardcover. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. 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 is the second Python edition of Machine Learning for Business Analytics. This edition also includes: A new chapter on generative AI (large language models or LLMs, and image generation)An expanded chapter on deep learningA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter of cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises with dataA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: New. 2025. 2nd Edition. hardcover. . . . . .

Condizione: New. 2nd edition NO-PA16APR2015-KAP.

Hardcover. Condizione: Brand New. 2nd edition. 688 pages. 11.02x8.66x2.76 inches. In Stock.

Hardback. Condizione: New. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. 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 is the second Python edition of Machine Learning for Business Analytics. This edition also includes: A new chapter on generative AI (large language models or LLMs, and image generation)An expanded chapter on deep learningA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter of cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises with dataA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, 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.

Condizione: New.

Condizione: New. 2025. 2nd Edition. hardcover. . . . . . Books ship from the US and Ireland.

Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Buch. Condizione: Neu. Neuware - Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.

Buch. Condizione: Neu. Machine Learning for Business Analytics | Concepts, Techniques, and Applications in Python | Galit Shmueli (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | John Wiley & Sons Inc | EAN 9781394286799 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Hardcover. Condizione: new. Hardcover. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. 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 is the second Python edition of Machine Learning for Business Analytics. This edition also includes: A new chapter on generative AI (large language models or LLMs, and image generation)An expanded chapter on deep learningA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter of cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises with dataA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. Brand new! Please provide a physical shipping address.

Hardback. Condizione: New. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. 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 is the second Python edition of Machine Learning for Business Analytics. This edition also includes: A new chapter on generative AI (large language models or LLMs, and image generation)An expanded chapter on deep learningA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter of cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises with dataA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, 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.

Hardcover. Condizione: new. Hardcover. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. 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 is the second Python edition of Machine Learning for Business Analytics. This edition also includes: A new chapter on generative AI (large language models or LLMs, and image generation)An expanded chapter on deep learningA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter of cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises with dataA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.