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
Автор: Murugan Anandarajan; Chelsey Hill; Thomas Nolan Название: Practical Text Analytics ISBN: 3030070808 ISBN-13(EAN): 9783030070809 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
Whether you're a marketer with development skills or a full-on web developer/analyst, Practical Google Analytics and Google Tag Manager for Developers shows you how to implement Google Analytics using Google Tag Manager to jumpstart your web analytics measurement.
There's a reason that so many organizations use Google Analytics. Effective collection of data with Google Analytics can reduce customer acquisition costs, provide priceless feedback on new product initiatives, and offer insights that will grow a customer or client base. So where does Google Tag Manager fit in?
Google Tag Manager allows for unprecedented collaboration between marketing and technical teams, lightning fast updates to your site, and standardization of the most common tags for on-site tracking an
d marketing efforts. To achieve the rich data you're really after to better serve your users' needs, you'll need the tools Google Tag Manager provides for a best-in-class implementation of Google Analytics measurement on your site.
Written by data evangelist and Google Analytics expert Jonathan Weber and the team at LunaMetrics, this book offers foundational knowledge, a collection of practical Google Tag Manager recipes, well-tested best practices, and troubleshooting tips to get your implementation in tip-top condition. It covers topics including:
- Google Analytics implementation via Google Tag Manager
- How to customize Google Analytics for your unique situation
- Using Google Tag Manager to track and analyze interactions across multiple devices and touch points
- How to extract data from Google Analytics and use Google BigQuery to analyze Big Data questions
What You'll Learn
Implementation approaches for Google Analytics, including common pitfalls and troubleshooting strategies.
How to use tools like Google Tag Manager and jQuery to jumpstart your Google Analytics implementation.
How to track metrics beyond page views to other critical user interactions, such as clicks on outbound links or downloads, scrolling and page engagement, usage of AJAX forms, and much more.
How to incorporate additional, customized data into Google Analytics to track individual users or enrich data about their behavior.
Who This Book Is For
Web developers, data analysts, and marketers with a basic familiarity with Google Analytics from an end-user perspective, as well as some knowledge of HTML and JavaScript.
IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.
Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit products
Concentrates on specific aspects of the modelling process by focusing on lifetime estimates
Provides an hands-on approach to enable readers to perform model development, validation and audit of credit risk models
Автор: Andreas Fran?ois Vermeulen Название: Practical Data Science ISBN: 1484230531 ISBN-13(EAN): 9781484230534 Издательство: Springer Рейтинг: Цена: 5309.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.
What You'll Learn
Become fluent in the essential concepts and terminology of data science and data engineering
Build and use a technology stack that meets industry criteria
Master the methods for retrieving actionable business knowledge
Coordinate the handling of polyglot data types in a data lake for repeatable results
Who This Book Is For
Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
Автор: Gennady Andrienko; Natalia Andrienko; Peter Bak; D Название: Visual Analytics of Movement ISBN: 3662507285 ISBN-13(EAN): 9783662507285 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces a general conceptual framework for the analysis of movement data from various sources. It illustrates all algorithms and methods with the help of sample applications from various domains.
Автор: Murugan Anandarajan Название: Aligning business strategies and analytics. ISBN: 3319932985 ISBN-13(EAN): 9783319932989 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines issues related to the alignment of business strategies and analytics. Chapter 4 focuses on a case study of ARI, a leading fleet management company, and explores the application of advanced analytics to various facets of the industry and the company`s experience in aligning analytics with its business strategy.
Автор: Baesens Bart Название: Analytics in a big data world ISBN: 1118892704 ISBN-13(EAN): 9781118892701 Издательство: Wiley Рейтинг: Цена: 6178.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
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.
Автор: Winters Ralph Название: Practical Predictive Analytics ISBN: 1785886185 ISBN-13(EAN): 9781785886188 Издательство: Неизвестно Рейтинг: Цена: 10114.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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