Handbook of Statistical Analysis: AI and ML Applications

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 solutionsProvides 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 algorithmsFeatures clear, intuitive explanations of standard analytical tools and techniques and their practical applicationsProvides a number of case studies to guide practitioners in the design of analytical applications to solve real-world problems in their data domainOffers valuable tutorials on the book webpage with step-by-step instructions on how to use suggested tools to build modelsProvides 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 INDICE: Part I - Introduction1. Historical Background to Analytics2. Theory3. Data Mining and Predictive Analytic Process4. Data Science Tool Types: Which one is Best?Part II - Data Preparation5. Data Access6. Data Understanding7. Data Visualization8. Data Cleaning9. Data Conditioning10. Feature Engineering11. Feature Selection12. Data Preparation CookbookPart III - Modeling13. Algorithms14. Modeling15. Model Evaluation and Enhancement16. Ensembles & Complexity17. Deep Learning vs. Traditional ML18. Explainable AI (XAI) put after Deep Learning19. Human in the LoopPart IV - Applications20. GENERAL OVERVIEW of an Application - Healthcare Delivery and Medical Informatics21. Specific Application: Business: Customer Response22. Specific Application: Education: Learning Analytics23. Specific Application: Medical Informatics: Colon Cancer Screening24. Specific Application: Financial: Credit Risk25. Specific FUTURE Application: The ‘INTELLIGENCE AGE (Revolution)’: LLMs like ChatGPT - Tiny ML - H.U.M.A.N.E. - Etc.Part V - Right Models - Luck - & Ethics of Analytics26. Right Model for the Right Use27. Ethics in Data Science28. Significance of LuckPart VI - Tutorials and Case StudiesTutorial A Example of Data Mining Recipes Using Statistica Data Miner 13Tutorial B Analysis of Hurricane Data (Hurrdata.sta) Using the Statistica Data Miner 13Tutorial C Predicting Student Success at High-Stakes Nursing Examinations (NCLEX) Using SPSS Modeler and Statistica Data Miner 13Tutorial D Constructing a Histogram Using MidWest Company Personality Data Using KNIMETutorial E Feature Selection Using KNIMETutorial F Medical/Business Tutorial Using Statistica Data Miner 13Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F (RAN note: This tutorial refers to the data used in Tutorial I, and it should be changed to refer to Tutorial F. I propose a new title: Tutorial G Medical/Business Tutorial with Tutorial F Data Using KNIME.Tutorial H Data Prep 1-1: Merging Data Sources Using KNIMETutorial I Data Prep 1-2: Data Description Using KNIMETutorial J Data Prep 2-1: Data Cleaning and Recoding Using KNIMETutorial K Data Prep 2-2: Dummy Coding Category Variables Using KNIMETutorial L Data Prep 2-3: Outlier Handling Using KNIMETutorial M Data Prep 3-1: Filling Missing Values With Constants Using KNIMETutorial N Data Prep 3-2: Filling Missing Values With Formulas Using KNIMETutorial O Data Prep 3-3: Filling Missing Values With a Model Using KNIMEBack Matter:Appendix-A - Listing of TUTORIALS and other RESOUCES on this book’s COMPANION WEB PAGEAppendix B - Instructions on HOW TO USE this book’s COMPANION WEB PAGE

• ISBN: 978-0-443-15845-2
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 650
• Fecha Publicación: 16/09/2024
• Nº Volúmenes: 1
• Idioma: Inglés