Fundamentals Of Big Data Data Mining And Machine Learning
DOWNLOAD
Download Fundamentals Of Big Data Data Mining And Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Fundamentals Of Big Data Data Mining And Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Fundamentals Of Big Data Data Mining And Machine Learning
DOWNLOAD
Author : Tarunika Chaudhari, Kamlesh W. Kelwade, K. Jasmine Mystica, M. Amshavalli
language : en
Publisher: RK Publication
Release Date : 2025-04-12
Fundamentals Of Big Data Data Mining And Machine Learning written by Tarunika Chaudhari, Kamlesh W. Kelwade, K. Jasmine Mystica, M. Amshavalli and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-12 with Computers categories.
This book offers a comprehensive introduction to Big Data, Data Mining, and Machine Learning, exploring foundational concepts, techniques, and real-world applications. It provides readers with essential tools for data analysis, pattern discovery, and predictive modeling, making it ideal for students, researchers, and professionals in data science and related fields.
Machine Learning And Big Data
DOWNLOAD
Author : Uma N. Dulhare
language : en
Publisher: John Wiley & Sons
Release Date : 2020-09-01
Machine Learning And Big Data written by Uma N. Dulhare and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-01 with Computers categories.
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.
Internet Of Things And Data Analytics Handbook
DOWNLOAD
Author : Hwaiyu Geng
language : en
Publisher: John Wiley & Sons
Release Date : 2017-01-10
Internet Of Things And Data Analytics Handbook written by Hwaiyu Geng and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-10 with Technology & Engineering categories.
This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view. Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing for IoT projects. It provides readers with knowledge in planning, designing, and implementing IoT projects. The book is written by experts on the subject matter, including international experts from nine countries in the consumer and enterprise fields of IoT. The text starts with an overview and anatomy of IoT, ecosystem of IoT, communication protocols, networking, and available hardware, both present and future applications and transformations, and business models. The text also addresses big data analytics, machine learning, cloud computing, and consideration of sustainability that are essential to be both socially responsible and successful. Design and implementation processes are illustrated with best practices and case studies in action. In addition, the book: Examines cloud computing, data analytics, and sustainability and how they relate to IoT overs the scope of consumer, government, and enterprise applications Includes best practices, business model, and real-world case studies Hwaiyu Geng, P.E., is a consultant with Amica Research (www.AmicaResearch.org, Palo Alto, California), promoting green planning, design, and construction projects. He has had over 40 years of manufacturing and management experience, working with Westinghouse, Applied Materials, Hewlett Packard, and Intel on multi-million high-tech projects. He has written and presented numerous technical papers at international conferences. Mr. Geng, a patent holder, is also the editor/author of Data Center Handbook (Wiley, 2015).
Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition
DOWNLOAD
Author : John D. Kelleher
language : en
Publisher: MIT Press
Release Date : 2020-10-20
Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition 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 2020-10-20 with Computers categories.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Big Data Fundamentals
DOWNLOAD
Author : Thomas Erl
language : en
Publisher: Prentice Hall
Release Date : 2015-12-29
Big Data Fundamentals written by Thomas Erl and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-29 with Computers categories.
“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
Data Mining And Analysis
DOWNLOAD
Author : Mohammed J. Zaki
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-12
Data Mining And Analysis written by Mohammed J. Zaki 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 2014-05-12 with Computers categories.
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Machine Learning And Big Data With Kdb Q
DOWNLOAD
Author : Paul A. Bilokon
language : en
Publisher:
Release Date : 2019-11-11
Machine Learning And Big Data With Kdb Q written by Paul A. Bilokon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with categories.
Upgrade your programming language to more effectively handle high-frequency data Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading. The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing "bible"-type reference, this book is designed with a focus on real-world practicality to help you quickly get up to speed and become productive with the language. Understand why kdb+/q is the ideal solution for high-frequency data Delve into "meat" of q programming to solve practical economic problems Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data - more variables, more metrics, more responsiveness and altogether more "moving parts." Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.
Fundamentals Of Data Science Datamining Machinelearning Deeplearning And Iots
DOWNLOAD
Author : Dr. P. Kavitha
language : en
Publisher: Leilani Katie Publication
Release Date : 2023-12-23
Fundamentals Of Data Science Datamining Machinelearning Deeplearning And Iots written by Dr. P. Kavitha 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 2023-12-23 with Computers categories.
Dr. P. Kavitha, Associate Professor, Department of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India. Mr. P. Jayasheelan, Assistant Professor, Department of Computer Science, Sri Krishna Aditya College of arts and Science, Coimbatore, Tamil Nadu, India. Ms. C. Karpagam, Assistant Professor, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India. Dr. K. Prabavathy, Assistant Professor, Department of Data Science and Analytics, Sree Saraswathi Thyagaraja College, Pollachi, Coimbatore, Tamil Nadu, India.
Hands On Data Science And Python Machine Learning
DOWNLOAD
Author : Frank Kane
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-31
Hands On Data Science And Python Machine Learning written by Frank Kane and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-31 with Computers categories.
This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.
Big Data
DOWNLOAD
Author : Rob Botwright
language : en
Publisher: Rob Botwright
Release Date : 2024
Big Data written by Rob Botwright and has been published by Rob Botwright this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Computers categories.
Uncover the secrets of Big Data with our comprehensive book bundle: "Big Data: Statistics, Data Mining, Analytics, and Pattern Learning." Dive into the world of data analytics and processing with Book 1, where you'll gain a solid understanding of the fundamentals necessary to navigate the vast landscape of big data. In Book 2, explore data mining techniques that allow you to extract valuable insights and patterns from large datasets. From marketing to finance and beyond, discover how to uncover hidden trends that drive informed decision-making. Ready to take your skills to the next level? Book 3 delves into advanced data science, where you'll learn to harness the power of machine learning for big data analysis. From regression analysis to neural networks, master the tools and techniques that drive predictive modeling and pattern recognition. Finally, in Book 4, learn how to design robust big data architectures that can scale to meet the needs of modern enterprises. Explore architectural patterns, scalability techniques, and fault tolerance mechanisms that ensure your systems are resilient and reliable. Whether you're a beginner looking to build a solid foundation or an experienced professional seeking to deepen your expertise, this book bundle has something for everyone. Don't miss out on this opportunity to unlock the potential of Big Data and drive innovation in your organization. Order now and embark on your journey to becoming a Big Data expert!