A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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>
Автор: Marsland Название: Machine Learning ISBN: 1466583282 ISBN-13(EAN): 9781466583283 Издательство: Taylor&Francis Рейтинг: Цена: 5537 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This bestseller helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Along with improved Python code, this second edition includes two new chapters on deep belief networks and Gaussian processes. It incorporates new material on the support vector machine, random forests, the perceptron convergence theorem, filters, and more. All of the code is available on the author’s website.
Описание: This book provides a thorough introduction to the most important topics in data mining and machine learning. All the topics covered have undergone rapid development and this treatment offers a modern perspective emphasizing the most recent contributions.
Описание: Examining the connections between these two increasingly intertwined areas, this text presents a unifying, thorough, and accessible introduction to the basic ideas and latest developments in machine learning and bioinformatics. It describes the major problems in bioinformatics and the concepts and algorithms of machine learning. The authors demonstrate the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks, and apply state-of-the-art techniques to bioinformatics problems in structural biology, cancer treatment, and proteomics. They also include exercises at the end of some chapters and offer instructional materials on their website.
Автор: Hardoon Название: Getting Started with Business Analytics ISBN: 1439896534 ISBN-13(EAN): 9781439896532 Издательство: Taylor&Francis Рейтинг: Цена: 4388 р. Наличие на складе: Поставка под заказ.
Описание: Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework of data mining in business analytics. It explores the contents, capabilities, and applications of business analytics without assuming any prior knowledge or technical skills. The authors describe business analytics from a non-commercial standpoint, demystify the main concepts and terminologies, and give many examples of real-world applications. They take you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.
Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Описание: The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models. Previous chapters have also been updated to reflect new developments in Machine Learning, and correct any previous errors in the text.
Описание: "Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security.
Описание: Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important, because it enables modeling and knowledge extraction from abundant data availability.Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. This book presents practical soft-computing approaches in data mining.Soft Computing for Knowledge Discovery and Data Mining was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Practitioners and researchers will be particularly interested in the description of real world data mining projects performed with soft computing. The book is also suitable for advanced-level students in computer science.
Автор: Bull Название: Learning Classifier Systems in Data Mining ISBN: 3540789782 ISBN-13(EAN): 9783540789789 Издательство: Springer Рейтинг: Цена: 15728 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes the main forms of Learning Classifier System. This book describes research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles and knowledge discovery.
Описание: MASTER THE ABILITY TO APPLY BIG DATA ANALYTICS TO MASSIVE AMOUNTS OF STRUCTURED AND UNSTRUCTURED DATA Cognitive computing is a technique that allows humans and computers to collaborate in order to gain insights and knowledge from data by uncovering patterns and anomalies. This comprehensive guide explains the underlying technologies, such as artificial intelligence, machine learning, natural language processing, and big data analytics. It then demonstrates how you can use these technologies to transform your organization. You will explore how different vendors and different industries are applying this emerging technology to help customers gain insights and take actions from their data. You will study detailed case histories from the financial, healthcare, and manufacturing industries, with step–by–step examinations of the design and testing of cognitive systems. You will benefit from the expert perspectives of organizations such as Welltok, Cleveland Clinic, and Memorial Sloan–Kettering as well as commercial vendors such as IBM, Google, Amazon, Hitachi, Dell, Cisco, and Numenta that are creating solutions, and demonstrating real–world implementation of cognitive computing systems. This book will go into detail about IBM s Watson platform and how it has influenced the development of cognitive computing. Cognitive systems are ushering in a new era of computing. In this book, you will learn how these technologies can enable emerging firms to compete with entrenched giants and forward–thinking, established organizations to disrupt their industries. You will gain both the theoretical and practical guidance you need to apply this technology, including: How cognitive computing is evolving from promise to reality Foundational services that are part of a cognitive computing system The distinguishing features of a cognitive computing system and how they work How to determine the underlying advanced analytics that support the development of a cognitive system The role of cloud and distributed computing Techniques for building a cognitive application Ways to leverage cognitive computing capabilities to transform your organization
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