Machine Learning and Big Data
Title | Machine Learning and Big Data PDF eBook |
Author | Uma N. Dulhare |
Publisher | John Wiley & Sons |
Pages | 544 |
Release | 2020-09-01 |
Genre | Computers |
ISBN | 1119654742 |
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Learning With Big Data
Title | Learning With Big Data PDF eBook |
Author | Viktor Mayer-Schönberger |
Publisher | HarperCollins |
Pages | 63 |
Release | 2014-03-04 |
Genre | Education |
ISBN | 0544355504 |
Homework assignments that learn from students. Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring in how we measure students’ progress and how we can use that data to improve education for everyone, in real time, both on- and offline. Learning with Big Data offers an eye-opening, insight-packed tour through these new trends, for educators, administrators, and readers interested in the latest developments in business and technology.
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
Title | Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges PDF eBook |
Author | Aboul Ella Hassanien |
Publisher | Springer Nature |
Pages | 648 |
Release | 2020-12-14 |
Genre | Computers |
ISBN | 303059338X |
This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
Big Data and Learning Analytics in Higher Education
Title | Big Data and Learning Analytics in Higher Education PDF eBook |
Author | Ben Kei Daniel |
Publisher | Springer |
Pages | 287 |
Release | 2016-08-27 |
Genre | Education |
ISBN | 3319065203 |
This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.
Big Data in Education
Title | Big Data in Education PDF eBook |
Author | Ben Williamson |
Publisher | SAGE |
Pages | 281 |
Release | 2017-07-24 |
Genre | Education |
ISBN | 1526416328 |
Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment!
Demystifying Big Data and Machine Learning for Healthcare
Title | Demystifying Big Data and Machine Learning for Healthcare PDF eBook |
Author | Prashant Natarajan |
Publisher | CRC Press |
Pages | 227 |
Release | 2017-02-15 |
Genre | Medical |
ISBN | 1315389304 |
Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.
Applications of Machine Learning in Big-Data Analytics and Cloud Computing
Title | Applications of Machine Learning in Big-Data Analytics and Cloud Computing PDF eBook |
Author | Subhendu Kumar Pani |
Publisher | CRC Press |
Pages | 346 |
Release | 2022-09-01 |
Genre | Technology & Engineering |
ISBN | 1000793559 |
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.