[math-science-tech] Handbook of Statistical Analysis: AI and ML Applications by Robert Nisbet PDF

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Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation.

This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application.

This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems.
Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data analytics to build successful predictive analytic solutions
Provides in-depth descriptions and directions for performing many data preparation operations necessary to generate data sets in the proper form and format for submission to modeling algorithms
Features clear, intuitive explanations of standard analytical tools and techniques and their practical applications
Provides a number of case studies to guide practitioners in the design of analytical applications to solve real-world problems in their data domain
Offers valuable tutorials on the book webpage with step-by-step instructions on how to use suggested tools to build models
Provides predictive insights into the rapidly expanding “Intelligence Age” as it takes over from the “Information Age,” enabling readers to easily transition the book’s content into the tools of the future


ISBN: 9780443158452
Publisher: Academic Press, an imprint of Elsevier; (December 27, 2024)
Number of pages (Paperback):‎ 650 pages
Number of pages (PDF):‎ 461 pages

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