Data Science E Machine Learning
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Machine Learning For Data Science Handbook
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Author : Lior Rokach
language : en
Publisher: Springer Nature
Release Date : 2023-08-17
Machine Learning For Data Science Handbook written by Lior Rokach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Computers categories.
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
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.
Intelligent Computing And Innovation On Data Science
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Author : Sheng-Lung Peng
language : en
Publisher: Springer Nature
Release Date : 2021-09-27
Intelligent Computing And Innovation On Data Science written by Sheng-Lung Peng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with Technology & Engineering categories.
This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man.
Data Scientist Pocket Guide
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Author : Mohamed Sabri
language : en
Publisher: BPB Publications
Release Date : 2021-06-24
Data Scientist Pocket Guide written by Mohamed Sabri and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-24 with Computers categories.
Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z
A Hands On Introduction To Data Science
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Author : Chirag Shah
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-02
A Hands On Introduction To Data Science written by Chirag Shah and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Business & Economics categories.
An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.
Data Science And Machine Learning
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Author : Michele Di Nuzzo
language : en
Publisher:
Release Date : 2022-03-31
Data Science And Machine Learning written by Michele Di Nuzzo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with categories.
Extracting knowledge from information through data analysis: the data scientist has been called the most attractive profession of the 21st century. Analyze the relationships between data, discover new information and, thanks to machine learning, exploit the immense potential hidden in it by building predictive models. In this book, we illustrate methods to analyze and manipulate data, and Machine Learning and Deep Learning algorithms to predict information, moving from theoretical knowledge to practical applications with statistical software R, through extensive practical examples What you will learn Mathematics and algebra for machine learning Statistics and probability for data science Use of the statistical software R and R-Studio Data preparation and feature engineering Design and validate machine learning algorithms Regression, classification and clustering algorithms Making predictions based on time series The models of neural networks and deep learning Data visualization & data storytelling Who this book is for This book is for anyone who wants to learn how to manipulate and analyze data by drawing new knowledge from it. If you are an IT manager or an analyst who wants to enter the world of Data Science and Big Data, if you are a developer who wants to know the new trends in the field of Artificial Intelligence or you are simply curious about this world, then this book is for you.
Introduction To Data Science And Machine Learning
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Author : Keshav Sud
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-03-25
Introduction To Data Science And Machine Learning written by Keshav Sud and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-25 with Computers categories.
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.
Data Science And Machine Learning
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Author : Dirk P. Kroese
language : en
Publisher: CRC Press
Release Date : 2019-11-20
Data Science And Machine Learning written by Dirk P. Kroese and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Business & Economics categories.
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
Data Analytics In E Learning Approaches And Applications
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Author : Marian Cristian Mihăescu
language : en
Publisher: Springer Nature
Release Date : 2022-03-22
Data Analytics In E Learning Approaches And Applications written by Marian Cristian Mihăescu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-22 with Computers categories.
This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
Targeted Learning In Data Science
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Author : Mark J. van der Laan
language : en
Publisher: Springer
Release Date : 2018-03-28
Targeted Learning In Data Science written by Mark J. van der Laan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-28 with Mathematics categories.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.