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 constitutes revised selected papers from the Second International Workshop on Future and Emerging Trends in Language Technology, FETLT 2016, which took place in Seville, Spain, in November 2016. The 10 full papers and 5 position papers presented in this volume were carefully reviewed and selected from 18 submissions.
Автор: Han Liu; Alexander Gegov; Mihaela Cocea Название: Rule Based Systems for Big Data ISBN: 3319236954 ISBN-13(EAN): 9783319236957 Издательство: Springer Рейтинг: Цена: 13677.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented.
Автор: Bairong Shen Название: Healthcare and Big Data Management ISBN: 9811060401 ISBN-13(EAN): 9789811060403 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book addresses the interplay of healthcare and big data management. Thanks to major advances in big data technologies and precision medicine, healthcare is now becoming the new frontier for both scientific research and economic development. This volume covers a range of aspects, including: big data management for healthcare;
Автор: Butch Quinto Название: Next-Generation Big Data ISBN: 1484231465 ISBN-13(EAN): 9781484231463 Издательство: Springer Рейтинг: Цена: 4268.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies.
Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard.
What You'll Learn
Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice
Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark
Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing
Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing
Turbocharge Spark with Alluxio, a distributed in-memory storage platform
Deploy big data in the cloud using Cloudera Director
Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark
Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks
Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling
Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard
Who This Book Is For Business intelligence and data warehouse professionals who are interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark. Experienced big data professionals who would like to learn more about Kudu and other advanced enterprise topics such as real-time data ingestion and complex event processing, Internet of Things, distributed in-memory big data computing, big data cloud deployments, big data governance, metadata management, real-time data visualization, data wrangling, data warehouse optimization and big data warehousing will also benefit from this book.
Описание: This book presents machine learning models and algorithms to address big data classification problems. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The third part presents the topics required to understand and select machine learning techniques to classify big data.
Автор: Panos Pardalos; Mario Pavone; Giovanni Maria Farin Название: Machine Learning, Optimization, and Big Data ISBN: 3319279254 ISBN-13(EAN): 9783319279251 Издательство: Springer Рейтинг: Цена: 6830.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This bookconstitutes revised selected papers from the First International Workshop onMachine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily,Italy, in July 2015. The 32papers presented in this volume were carefully reviewed and selected from 73submissions.
Автор: Karthik S., Paul Anand, Karthikeyan N. Название: Deep Learning Innovations and Their Convergence with Big Data ISBN: 1522530150 ISBN-13(EAN): 9781522530152 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29938.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics.Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection
Автор: Guida Название: Big Data and Machine Learning in Quantitative Investment ISBN: 1119522196 ISBN-13(EAN): 9781119522195 Издательство: Wiley Рейтинг: Цена: 6653.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Get to know the 'why' and 'how' of machine learning and big data in quantitative investment
Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance.
The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning.
- Gain a solid reason to use machine learning
- Frame your question using financial markets laws
- Know your data
- Understand how machine learning is becoming ever more sophisticated
Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment -- and this book shows you how.
Автор: Pendyala Название: Veracity of Big Data ISBN: 1484236327 ISBN-13(EAN): 9781484236321 Издательство: Springer Рейтинг: Цена: 4024.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: Introduction Chapter Goal: Introduce the readers to the manifestations of falsehood in Big Data and its ramifications.No of pages 30Sub -Topics1. The Big Data Phenomenon2. The Four V's3. Veracity - the fourth 'V'4. Tracing truth in human endeavors5. Veracity in the context of the WebChapter 2: Mathematical AbstractionChapter Goal: Present the math behind the method and develop a mathematical framework within which the problem and its solution can be discussed.No of pages: 30Sub - Topics 1. A fruit vendor example2. Building the abstraction3. Twitter Example - Sentiment Analysis4. Solution SpaceChapter 3: Tools and TechniquesChapter Goal: Introduce the Machine Learning and mathematical tools to solve the problem. No of pages: 30Sub - Topics: 1. Machine Learning Algorithms - a quick primer2. Kalman Filter3. Statistical Techniques Chapter 4: Veracity of Web InformationChapter Goal: Use the concepts, tools, and techniques described in chapter 3 to examine the truthfulness of microblogsNo of pages: 50Sub - Topics: 1. Machine Learning the truthfulness of twitter data2. Statistical approaches to detect veiled attacks3. Applying Kalman Filter to analyze sentiment fluctuations Chapter 5: Future DirectionsChapter Goal: Explore ideas that the readers can consider for further delving into the topic, given that this is a niche area.1. Natural Language Processing methods2. Knowledge Representation Techniques3. Ensemble Methods
Описание: This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes.
Автор: Mittal Название: Big Data Processing Using Spark in Cloud ISBN: 9811305498 ISBN-13(EAN): 9789811305498 Издательство: Springer Рейтинг: Цена: 12196.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book describes the emergence of big data technologies and the role of Spark in the entire big data stack.
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