Описание: In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Описание: This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Описание: For centuries people have recognised the importance of language in creating and applying law. This edited volume shows scholars and students how modern linguistics and related fields contribute to understanding the role language plays, and what follows from viewing law`s power as a matter of situated communication in specific social relations rather than an abstract system of rules.
Описание: Sometimes you get tired, doing this thing we call justice. You feel burned out or disillusioned. Sometimes you just need a word from the Lord. In these daily devotions, Donna Barber offers life-giving words of renewal and hope for those engaged in the resistance to injustice. When your legs are tired from marching and your knees are bruised from kneeling, you can experience rest and healing.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Transform your app ideas into fully functional prototypes with the help of expert tips and best practices from Mendix partners
Key Features
Meet the ever-increasing demand for software solution delivery without having to write any code
Build high-availability, low-cost applications unlike those developed via a traditional software engineering approach
Explore Mendix from product design through to delivery using real-world scenarios
Book Description
Low-code is a visual approach to application development. It enables developers of varying experience levels to create web and mobile apps using drag-and-drop components and model-driven logic through a graphic user interface. Mendix is among the fastest-growing platforms that enable low-code enthusiasts to put their software ideas into practice without having to write much code, and Building Low-Code Applications with Mendix will help you get up and running with the process using examples and practice projects.
The book starts with an introduction to Mendix, along with the reasons for using this platform and its tools for creating your first app. As you progress, you'll explore Mendix Studio Pro, the visual environment that will help you learn Mendix app creation. Once you have your working app ready, you'll understand how to enhance it with custom business logic and rules. Next, you'll find out how to defend your app against bad data, troubleshoot and debug it, and finally, connect it with real-world business platforms. You'll build practical skills as the book is filled with examples, real-world scenarios, and explanations of the tools needed to help you build low-code apps successfully.
By the end of this book, you'll have understood the concept of low-code development, learned how to use Mendix effectively, and developed a working app.
What You Will Learn
Gain a clear understanding of what low-code development is and the factors driving its adoption
Become familiar with the various features of Mendix for rapid application development
Discover concrete use cases of Studio Pro
Build a fully functioning web application that meets your business requirements
Get to grips with Mendix fundamentals to prepare for the Mendix certification exam
Understand the key concepts of app development such as data management, APIs, troubleshooting, and debugging
Who this book is for
This book is for tech-savvy business analysts and citizen developers who want to get started with Mendix for rapid mobile and web application development. The book is also helpful for seasoned developers looking to learn a new tool/platform and for anyone passionate about designing technical solutions without wanting to indulge in the complexities of writing code. The book assumes beginner-level knowledge of object-oriented programming and the ability to translate technical solutions from business requirements.
Описание: The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management.
Описание: Features innovative research and implementation practices of analytics in marketing research. Highlighting various techniques in acquiring and deciphering marketing data, this publication is a pivotal reference for professionals, managers, market researchers, and practitioners interested in the observation and utilization of data on marketing trends to promote positive business practices.
Автор: Lee T. Ostrom, Cheryl A. Wilhelmsen Название: Risk Assessment: Tools, Techniques, and Their Applications ISBN: 1119483468 ISBN-13(EAN): 9781119483465 Издательство: Wiley Рейтинг: Цена: 17733.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Guides the reader through a risk assessment and shows them the proper tools to be used at the various steps in the process
This brand new edition of one of the most authoritative books on risk assessment adds ten new chapters to its pages to keep readers up to date with the changes in the types of risk that individuals, businesses, and governments are being exposed to today. It leads readers through a risk assessment and shows them the proper tools to be used at various steps in the process. The book also provides readers with a toolbox of techniques that can be used to aid them in analyzing conceptual designs, completed designs, procedures, and operational risk.
Risk Assessment: Tools, Techniques, and Their Applications, Second Edition includes expanded case studies and real life examples; coverage on risk assessment software like SAPPHIRE and RAVEN; and end-of-chapter questions for students. Chapters progress from the concept of risk, through the simple risk assessment techniques, and into the more complex techniques. In addition to discussing the techniques, this book presents them in a form that the readers can readily adapt to their particular situation. Each chapter, where applicable, presents the technique discussed in that chapter and demonstrates how it is used.
Expands on case studies and real world examples, so that the reader can see complete examples that demonstrate how each of the techniques can be used in analyzing a range of scenarios
Includes 10 new chapters, including Bayesian and Monte Carlo Analyses; Hazard and Operability (HAZOP) Analysis; Threat Assessment Techniques; Cyber Risk Assessment; High Risk Technologies; Enterprise Risk Management Techniques
Adds end-of-chapter questions for students, and provides a solutions manual for academic adopters
Acts as a practical toolkit that can accompany the practitioner as they perform a risk assessment and allows the reader to identify the right assessment for their situation
Presents risk assessment techniques in a form that the readers can readily adapt to their particular situation
Risk Assessment: Tools, Techniques, and Their Applications, Second Edition is an important book for professionals that make risk-based decisions for their companies in various industries, including the insurance industry, loss control, forensics, all domains of safety, engineering and technical fields, management science, and decision analysis. It is also an excellent standalone textbook for a risk assessment or a risk management course.
Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained.
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