Encyclopedia Of Data Science And Machine Learning

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Encyclopedia Of Data Science And Machine Learning Vol 3

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Author : John Wang
language : en
Publisher: Encyclopedia of Data Science and Machine Learning
Release Date : 2022-10-14

Encyclopedia Of Data Science And Machine Learning Vol 3 written by John Wang and has been published by Encyclopedia of Data Science and Machine Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-14 with categories.

Encyclopedia Of Data Science And Machine Learning Vol 1

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Author : John Wang
language : en
Publisher: Encyclopedia of Data Science and Machine Learning
Release Date : 2022-10-14

Encyclopedia Of Data Science And Machine Learning Vol 1 written by John Wang and has been published by Encyclopedia of Data Science and Machine Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-14 with categories.

Encyclopedia Of Machine Learning And Data Mining

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Author : Claude Sammut
language : en
Publisher:
Release Date :

Encyclopedia Of Machine Learning And Data Mining written by Claude Sammut and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Machine learning categories.

Encyclopedia Of Machine Learning

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Author : Claude Sammut
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28

Encyclopedia Of Machine Learning written by Claude Sammut and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Computers categories.

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.

Encyclopedia Of Big Data Technologies

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Author : Sherif Sakr
language : en
Publisher: Springer
Release Date : 2019-03-01

Encyclopedia Of Big Data Technologies written by Sherif Sakr and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-01 with Computers categories.

The Encyclopedia of Big Data Technologies provides researchers, educators, students and industry professionals with a comprehensive authority over the most relevant Big Data Technology concepts. With over 300 articles written by worldwide subject matter experts from both industry and academia, the encyclopedia covers topics such as big data storage systems, NoSQL database, cloud computing, distributed systems, data processing, data management, machine learning and social technologies, data science. Each peer-reviewed, highly structured entry provides the reader with basic terminology, subject overviews, key research results, application examples, future directions, cross references and a bibliography. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals. In addition, the distinguished, international editorial board of the encyclopedia consists of well-respected scholars, each developing topics based upon their expertise.

Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions

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Author : Matt Taddy
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-08-23

Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions written by Matt Taddy and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Business & Economics categories.

Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

A Global Approach To Data Value Maximization

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Author : Paolo Dell’Aversana
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2019-04-17

A Global Approach To Data Value Maximization written by Paolo Dell’Aversana and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-17 with Computers categories.

This book presents a systematic discussion about methods and techniques used to extract the maximum informative value from complex data sets. A multitude of approaches and techniques can be applied for that purpose, including data fusion and model integration, multimodal data analysis in different physical domains, audio-video display of data through techniques of “sonification”, multimedia machine learning, and hybrid methods of data analysis. The book begins with the domain of geosciences, before moving on to other scientific areas, like diagnostic medicine and various engineering sectors. As such, it will appeal to a large audience, including geologists and geophysicists, data scientists, physicians and cognitive scientists, and experts in social sciences and knowledge management.

Encyclopedia Of Data Warehousing And Mining

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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2005-06-30

Encyclopedia Of Data Warehousing And Mining written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-30 with Computers categories.

Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.

Encyclopedia Of Machine Learning And Data Mining

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Author : Claude Sammut
language : en
Publisher: Springer
Release Date : 2017-03-15

Encyclopedia Of Machine Learning And Data Mining written by Claude Sammut and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-15 with Computers categories.

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.

Elgar Encyclopedia Of Law And Data Science

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Author : Comandé, Giovanni
language : en
Publisher: Edward Elgar Publishing
Release Date : 2022-02-18

Elgar Encyclopedia Of Law And Data Science written by Comandé, Giovanni and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-18 with Law categories.

This Encyclopedia brings together jurists, computer scientists, and data analysts to map the emerging field of data science and law for the first time, uncovering the challenges, opportunities, and fault lines that arise as these groups are increasingly thrown together by expanding attempts to regulate and adapt to a data-driven world. It explains the concepts and tools at the crossroads of the many disciplines involved in data science and law, bridging scientific and applied domains. Entries span algorithmic fairness, consent, data protection, ethics, healthcare, machine learning, patents, surveillance, transparency and vulnerability.