Big Data Data Mining And Machine Learning

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Big Data, Data Mining, and Machine Learning

Jared Dean
Big Data Data Mining and Machine Learning Book PDF

Author : Jared Dean
Publisher : John Wiley & Sons
Page : 288 pages
File Size : 48,7 Mb
Release : 2014-05-07
Category : Computers
ISBN : 9781118920701

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Big Data, Data Mining, and Machine Learning by Jared Dean Pdf

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Aboul Ella Hassanien,Ashraf Darwish
Machine Learning and Big Data Analytics Paradigms Analysis Applications and Challenges Book PDF

Author : Aboul Ella Hassanien,Ashraf Darwish
Publisher : Springer Nature
Page : 648 pages
File Size : 46,6 Mb
Release : 2020-12-14
Category : Computers
ISBN : 9783030593384

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges by Aboul Ella Hassanien,Ashraf Darwish Pdf

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Introduction to Algorithms for Data Mining and Machine Learning

Xin-She Yang
Introduction to Algorithms for Data Mining and Machine Learning Book PDF

Author : Xin-She Yang
Publisher : Academic Press
Page : 188 pages
File Size : 48,8 Mb
Release : 2019-07-15
Category : Mathematics
ISBN : 9780128172162

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Introduction to Algorithms for Data Mining and Machine Learning by Xin-She Yang Pdf

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Data Mining and Machine Learning

Mohammed J. Zaki,Wagner Meira, Jr
Data Mining and Machine Learning Book PDF

Author : Mohammed J. Zaki,Wagner Meira, Jr
Publisher : Cambridge University Press
Page : 779 pages
File Size : 48,6 Mb
Release : 2020-01-30
Category : Business & Economics
ISBN : 9781108473989

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Data Mining and Machine Learning by Mohammed J. Zaki,Wagner Meira, Jr Pdf

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Statistical and Machine-Learning Data Mining:

Bruce Ratner
Statistical and Machine Learning Data Mining Book PDF

Author : Bruce Ratner
Publisher : CRC Press
Page : 656 pages
File Size : 47,9 Mb
Release : 2017-07-12
Category : Computers
ISBN : 9781498797610

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Statistical and Machine-Learning Data Mining: by Bruce Ratner Pdf

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Statistical and Machine-Learning Data Mining

Bruce Ratner
Statistical and Machine Learning Data Mining Book PDF

Author : Bruce Ratner
Publisher : CRC Press
Page : 544 pages
File Size : 42,9 Mb
Release : 2012-02-28
Category : Business & Economics
ISBN : 9781466551213

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Statistical and Machine-Learning Data Mining by Bruce Ratner Pdf

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Data Mining and Machine Learning Applications

Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
Data Mining and Machine Learning Applications Book PDF

Author : Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
Publisher : John Wiley & Sons
Page : 500 pages
File Size : 49,7 Mb
Release : 2022-01-26
Category : Computers
ISBN : 9781119792505

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Data Mining and Machine Learning Applications by Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi Pdf

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Big Data Analytics Methods

Peter Ghavami
Big Data Analytics Methods Book PDF

Author : Peter Ghavami
Publisher : Walter de Gruyter GmbH & Co KG
Page : 254 pages
File Size : 50,8 Mb
Release : 2019-12-16
Category : Business & Economics
ISBN : 9781547401567

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Big Data Analytics Methods by Peter Ghavami Pdf

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Data Mining and Analysis

Mohammed J. Zaki,Wagner Meira, Jr,Wagner Meira
Data Mining and Analysis Book PDF

Author : Mohammed J. Zaki,Wagner Meira, Jr,Wagner Meira
Publisher : Cambridge University Press
Page : 607 pages
File Size : 46,6 Mb
Release : 2014-05-12
Category : Computers
ISBN : 9780521766333

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Data Mining and Analysis by Mohammed J. Zaki,Wagner Meira, Jr,Wagner Meira Pdf

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Encyclopedia of Machine Learning

Claude Sammut,Geoffrey I. Webb
Encyclopedia of Machine Learning Book PDF

Author : Claude Sammut,Geoffrey I. Webb
Publisher : Springer Science & Business Media
Page : 1061 pages
File Size : 41,8 Mb
Release : 2011-03-28
Category : Computers
ISBN : 9780387307688

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Encyclopedia of Machine Learning by Claude Sammut,Geoffrey I. Webb Pdf

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

BIG DATA ANALYTICS

Parag Kulkarni,Sarang Joshi,,Meta S. Brown
BIG DATA ANALYTICS Book PDF

Author : Parag Kulkarni,Sarang Joshi,,Meta S. Brown
Publisher : PHI Learning Pvt. Ltd.
Page : 208 pages
File Size : 43,5 Mb
Release : 2016-07-07
Category : Language Arts & Disciplines
ISBN : 9788120351165

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BIG DATA ANALYTICS by Parag Kulkarni,Sarang Joshi,,Meta S. Brown Pdf

The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

Data Science

Richard Hurley
Data Science Book PDF

Author : Richard Hurley
Publisher : Unknown
Page : 180 pages
File Size : 52,5 Mb
Release : 2019-11-02
Category : Big data
ISBN : 1704636035

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Data Science by Richard Hurley Pdf

If you want to learn about data science and big data, then keep reading... Two manuscripts in one book: Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in. There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today. Some of the topics covered in part 1 of this book include: What is Data Science? What Exactly Does a Data Scientist Do? A Look at What Data Analytics Is All About What is Data Mining and How Does It Fit in with Data Science? Regression Analysis Why is Data Visualization So Important When It Comes to Understanding Your Data? How to work with Database Querying A Look at Artificial Intelligence What is Machine Learning and How Is It Different from Artificial Intelligence? What is the Future of Artificial Intelligence and Machine Learning? And much more! Some of the topics covered in part 2 of this book include: What is big data, and why is it important? The five V's behind big data How big data is already impacting your life, and where big data may be headed How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things How companies and governments are using predictive analytics to get ahead of the competition or improve service How big data is used for fraud detection How big data can train intelligent computer systems The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics Upcoming trends in big data that are sure to have a large impact on your future Artificial intelligence, and how big data drives its development What machine learning is and how it is tied to big data The relationship between big data, data analytics, and business intelligence Insights into how big data impacts privacy issues The pros and cons regarding big data And much, much more! So if you want to learn more about data science and big data, click the "add to cart" button!

Feature Engineering for Machine Learning and Data Analytics

Guozhu Dong,Huan Liu
Feature Engineering for Machine Learning and Data Analytics Book PDF

Author : Guozhu Dong,Huan Liu
Publisher : CRC Press
Page : 389 pages
File Size : 41,6 Mb
Release : 2018-03-14
Category : Business & Economics
ISBN : 9781351721264

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Feature Engineering for Machine Learning and Data Analytics by Guozhu Dong,Huan Liu Pdf

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Big Data

Richard Hurley
Big Data Book PDF

Author : Richard Hurley
Publisher : Unknown
Page : 98 pages
File Size : 42,8 Mb
Release : 2019-09-28
Category : Electronic
ISBN : 1696133726

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Big Data by Richard Hurley Pdf

If you want to learn about big data, then keep reading... Do you want to understand what big data is all about, but you don't get all the hype? Are you intrigued by the idea of building a career around big data and data science, but you just don't understand it? If so, this book could be what you are looking for. In this book, we will explore the hot concept of big data and explain it for beginners like yourself. If you know nothing about big data now, you will come away with a good overview of the subject and how it integrates into the other hot technologies of the day widely adopted by business, such as artificial intelligence, predictive analytics, and machine learning. Even if you don't intend to get directly involved in big data, understanding it will be very important in the coming years. It is one of the most important phenomena to hit in many years. It took some time for technology to catch up so that big data could be analyzed and processed, but now we are in the midst of a data revolution. Big data powers many of the world's most powerful companies, including Facebook, Amazon, and Google, among others. In this book, you will learn: What is big data, and why is it important? The five V's behind big data How big data is already impacting your life, and where big data may be headed How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things How companies and governments are using predictive analytics to get ahead of the competition or improve service How big data is used for fraud detection How big data can train intelligent computer systems The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics Upcoming trends in big data that are sure to have a large impact on your future Artificial intelligence, and how big data drives its development What machine learning is and how it is tied to big data The relationship between big data, data analytics, and business intelligence Insights into how big data impacts privacy issues The pros and cons regarding big data And much, much more! If you want to learn about this new, exciting, and rapidly developing technology, then download this book right now! You will not be baffled by big data any longer, and you will understand the behavior of large companies far better than you ever did before.

Machine Learning for Data Streams

Albert Bifet,Ricard Gavalda,Geoffrey Holmes,Bernhard Pfahringer
Machine Learning for Data Streams Book PDF

Author : Albert Bifet,Ricard Gavalda,Geoffrey Holmes,Bernhard Pfahringer
Publisher : MIT Press
Page : 289 pages
File Size : 49,6 Mb
Release : 2023-05-09
Category : Computers
ISBN : 9780262547833

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Machine Learning for Data Streams by Albert Bifet,Ricard Gavalda,Geoffrey Holmes,Bernhard Pfahringer Pdf

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.