Machine Learning A Gateway To Data Science

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Machine Learning For Data Streams

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Author : Albert Bifet
language : en
Publisher: MIT Press
Release Date : 2018-03-16

Machine Learning For Data Streams written by Albert Bifet and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-16 with Computers categories.

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.

Data Science

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Author : John D. Kelleher
language : en
Publisher: MIT Press
Release Date : 2018-04-13

Data Science written by John D. Kelleher and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-13 with Computers categories.

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Encyclopedia Of Data Science And Machine Learning

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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20

Encyclopedia Of Data Science And Machine Learning 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 2023-01-20 with Computers categories.

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.

Machine Learning Optimization And Data Science

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Author : Giuseppe Nicosia
language : en
Publisher: Springer Nature
Release Date : 2020-01-03

Machine Learning Optimization And Data Science written by Giuseppe Nicosia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-03 with Computers categories.

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Data Science And Big Data An Environment Of Computational Intelligence

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Author : Witold Pedrycz
language : en
Publisher: Springer
Release Date : 2017-03-21

Data Science And Big Data An Environment Of Computational Intelligence written by Witold Pedrycz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-21 with Technology & Engineering categories.

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today’s knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

The 2020 International Conference On Machine Learning And Big Data Analytics For Iot Security And Privacy

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Author : John MacIntyre
language : en
Publisher: Springer Nature
Release Date : 2020-11-04

The 2020 International Conference On Machine Learning And Big Data Analytics For Iot Security And Privacy written by John MacIntyre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with Computers categories.

This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

Machine Learning A Gateway To Data Science

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Author : Mrs.S.N.Santhalakshmi
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-05-16

Machine Learning A Gateway To Data Science written by Mrs.S.N.Santhalakshmi and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Computers categories.

Mrs.S.N.Santhalakshmi, Assistant Professor & Head of The Department, Department of Computer Applications, Nandha Arts & Science College, Erode, Tamil Nadu, India. Dr.Goutam Panigrahi, Assistant Professor, Department of Mathematics, National Institute of Technology, Durgapur, West Bengal, India. Dr. Saibal Majumder, Assistant Professor, Department of Computer Science and Engineering(Data Science), Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India. Dr. Chandan Bandyopadhyay, Associate Professor & Head of the Department, Department of Computer Science and Engineering(Data Science), Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India.

Exam Ref 70 774 Perform Cloud Data Science With Azure Machine Learning

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Author : Ginger Grant
language : en
Publisher: Microsoft Press
Release Date : 2018-03-01

Exam Ref 70 774 Perform Cloud Data Science With Azure Machine Learning written by Ginger Grant and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-01 with Computers categories.

Prepare for Microsoft Exam 70-774–and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning Develop machine learning models Operationalize and manage Azure Machine Learning Services Use other services for machine learning This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are familiar with Azure data services, machine learning concepts, and common data science processes About the Exam Exam 70-774 focuses on skills and knowledge needed to prepare data for analysis with Azure Machine Learning; find key variables describing your data’s behavior; develop models and identify optimal algorithms; train, validate, deploy, manage, and consume Azure Machine Learning Models; and leverage related services and APIs. About Microsoft Certification Passing this exam as well as Exam 70-773: Analyzing Big Data with Microsoft R earns your MCSA: Machine Learning certifi¿cation, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services. See full details at: microsoft.com/learning

Proceedings Of International Conference On Computational Intelligence Data Science And Cloud Computing

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Author : Valentina E. Balas
language : en
Publisher: Springer Nature
Release Date : 2021-04-05

Proceedings Of International Conference On Computational Intelligence Data Science And Cloud Computing written by Valentina E. Balas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-05 with Technology & Engineering categories.

This book includes selected papers presented at International Conference on Computational Intelligence, Data Science and Cloud Computing (IEM-ICDC) 2020, organized by the Department of Information Technology, Institute of Engineering & Management, Kolkata, India, during 25–27 September 2020. It presents substantial new research findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry.

Fog Computing Deep Learning And Big Data Analytics Research Directions

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Author : C.S.R. Prabhu
language : en
Publisher: Springer
Release Date : 2019-01-04

Fog Computing Deep Learning And Big Data Analytics Research Directions written by C.S.R. Prabhu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-04 with Computers categories.

This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.