Encyclopedia of Data Science and Machine Learning, VOL 4
Author: John Wang
Publisher: Encyclopedia of Data Science and Machine Learning
Published: 2022-10-14
Total Pages: 0
ISBN-13: 9781668481646
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Encyclopedia of Data Science and Machine Learning, VOL 1
Author: John Wang
Publisher: Encyclopedia of Data Science and Machine Learning
Published: 2022-10-14
Total Pages: 0
ISBN-13: 9781668481615
DOWNLOAD EBOOK →Encyclopedia of Machine Learning
Author: Claude Sammut
Publisher: Springer Science & Business Media
Published: 2011-03-28
Total Pages: 1061
ISBN-13: 0387307680
DOWNLOAD EBOOK →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 Data Science and Machine Learning
Author: Wang, John
Publisher: IGI Global
Published: 2023-01-20
Total Pages: 3296
ISBN-13: 1799892212
DOWNLOAD EBOOK →Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Introduction to Data Science and Machine Learning
Author: Keshav Sud
Publisher: BoD – Books on Demand
Published: 2020-03-25
Total Pages: 233
ISBN-13: 1838803335
DOWNLOAD EBOOK →Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.
Elgar Encyclopedia of Law and Data Science
Author: Comandé, Giovanni
Publisher: Edward Elgar Publishing
Published: 2022-02-18
Total Pages: 400
ISBN-13: 1839104597
DOWNLOAD EBOOK →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.
Data Science and Machine Learning
Author: Dirk P. Kroese
Publisher: CRC Press
Published: 2019-11-20
Total Pages: 538
ISBN-13: 1000730778
DOWNLOAD EBOOK →Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Analysis and Forecasting of Financial Time Series
Author: Jaydip Sen
Publisher: Cambridge Scholars Publishing
Published: 2022-10-11
Total Pages: 405
ISBN-13: 1527588858
DOWNLOAD EBOOK →This book brings together real-world cases illustrating how to analyse volatile financial time series in order to provide a better understanding of their past behavior and robust forecasting of their future behavioural patterns. Using time series data from diverse financial sectors, it shows how the concepts and techniques of statistical analysis, machine learning, and deep learning are applied to build robust predictive models, as well as the ways in which these models can be used for forecasting the future prices of stocks and constructing profitable portfolios of investments. All the concepts and methods used in the book have been implemented using Python and R languages on TensorFlow and Keras frameworks. The volume will be particularly useful for advanced postgraduate and doctoral students of finance, economics, econometrics, statistics, data science, computer science, and information technology.
Encyclopedia of Machine Learning and Data Mining
Author: Claude Sammut
Publisher: Springer
Published: 2017-03-15
Total Pages: 0
ISBN-13: 9781489976857
DOWNLOAD EBOOK →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.
Advances in Machine Learning and Data Science
Author: Damodar Reddy Edla
Publisher: Springer
Published: 2018-05-16
Total Pages: 380
ISBN-13: 9811085692
DOWNLOAD EBOOK →The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms.
