CSU-Carig
Nisbet, Robert, author.
Handbook of statistical analysis : AI and ML applications / Robert Nisbet, Keith McCormick, Gary Miner. - 3rd edition. - London ; Academic Press : ©2024. - 1 online resource, 650 pages : color illustrations
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
9780443158469 (ebk)
Data mining -- Statistical analysis
Machine learning -- Artificial intelligence
Predictive analytics -- Data science
006.312 / N723 2025
Handbook of statistical analysis : AI and ML applications / Robert Nisbet, Keith McCormick, Gary Miner. - 3rd edition. - London ; Academic Press : ©2024. - 1 online resource, 650 pages : color illustrations
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
9780443158469 (ebk)
Data mining -- Statistical analysis
Machine learning -- Artificial intelligence
Predictive analytics -- Data science
006.312 / N723 2025