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Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel
- Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python
- Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel
- Page: 720
- Format: pdf, ePub, mobi, fb2
- ISBN: 9781394286799
- Publisher: Wiley
Download Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python
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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 learning A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 students A full chapter of cases demonstrating applications for the machine learning techniques End-of-chapter exercises with data A 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.
An Introduction to Statistical Learning
The first edition of this book, with applications in R (ISLR), was released in 2013. concepts in either R or Python. The chapters cover the following .
[PDF] Introduction to Machine Learning with Python
Chapter 1 introduces the fundamental concepts of machine learning and its applications, and describes the setup we will be using throughout the book.
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The two books are: Data Mining for Business Analytics: Concepts, Techniques and Applications in Python by Galit Shmueli, Peter C. Bruce, .
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It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, .
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The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods .
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Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods .
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Buy Machine Learning for Business Analytics: Concepts Techniques and Galit Shmueli 2nd edition or 2023 edition Wiley ISBN 1119835178 9780002007313.
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Use this textbook to help you learn and understand the core knowledge and techniques needed for analysing business data with Python programming.
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Machine Learning for Business Analytics Concepts, Techniques, and Applications with Analytic Solver Data Mining, Fourth Edition: Concepts, Techniques, .
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Book. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner. March 2023. Publisher: Wiley; ISBN: 978-1-119-82881-5.
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