Machine Learning For Big Data Analysis

Download Machine Learning For Big Data Analysis full books in PDF, epub, and Kindle. Read online Machine Learning For Big Data Analysis ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Applications of Machine Learning in Big-Data Analytics and Cloud Computing

Author : Subhendu Kumar Pani
Publisher : CRC Press
ISBN 13 : 1000793559
Total Pages : 346 pages
Book Rating : 4.0/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Applications of Machine Learning in Big-Data Analytics and Cloud Computing by : Subhendu Kumar Pani

Download or read book Applications of Machine Learning in Big-Data Analytics and Cloud Computing written by Subhendu Kumar Pani and published by CRC Press. This book was released on 2022-09-01 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Author : Aboul Ella Hassanien
Publisher : Springer Nature
ISBN 13 : 303059338X
Total Pages : 648 pages
Book Rating : 4.0/5 (35 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges by : Aboul Ella Hassanien

Download or read book Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges written by Aboul Ella Hassanien and published by Springer Nature. This book was released on 2020-12-14 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning Models and Algorithms for Big Data Classification

Author : Shan Suthaharan
Publisher : Springer
ISBN 13 : 1489976418
Total Pages : 359 pages
Book Rating : 4.4/5 (899 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning Models and Algorithms for Big Data Classification by : Shan Suthaharan

Download or read book Machine Learning Models and Algorithms for Big Data Classification written by Shan Suthaharan and published by Springer. This book was released on 2015-10-20 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

Python Machine Learning for Beginners

Author : Leonard Deep
Publisher :
ISBN 13 : 9781097858309
Total Pages : 236 pages
Book Rating : 4.8/5 (583 download)

DOWNLOAD NOW!

Book Synopsis Python Machine Learning for Beginners by : Leonard Deep

Download or read book Python Machine Learning for Beginners written by Leonard Deep and published by . This book was released on 2019-05-13 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you interested to get into the programming world? Do you want to learn and understand Python and Machine Learning? Python Machine Learning for Beginners is the guide for you. Python Machine Learning for Beginners is the ultimate guide for beginners looking to learn and understand how Python programming works. Python Machine Learning for Beginners is split up into easy to learn chapters that will help guide the readers through the early stages of Python programming. It's this thought out and systematic approach to learning which makes Python Machine Learning for Beginners such a sought-after resource for those that want to learn about Python programming and about Machine Learning using an object-oriented programming approach. Inside Python Machine Learning for Beginners you will discover: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Throughout the book, you will learn the basic concepts behind Python programming which is designed to introduce you to Python programming. You will learn about getting started, the keywords and statements, data types and type conversion. Along with different examples, there are also exercises to help ensure that the information sinks in. You will find this book an invaluable tool for starting and mastering Machine Learning using Python. Once you complete Python Machine Learning for Beginners, you will be more than prepared to take on any Python programming. Scroll back up to the top of this page and hit BUY IT NOW to get your copy of Python Machine Learning for Beginners! You won't regret it!

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Author : Pradeep N
Publisher : Academic Press
ISBN 13 : 0128220449
Total Pages : 374 pages
Book Rating : 4.1/5 (282 download)

DOWNLOAD NOW!

Book Synopsis Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics by : Pradeep N

Download or read book Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics written by Pradeep N and published by Academic Press. This book was released on 2021-06-10 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Machine Learning and Big Data

Author : Uma N. Dulhare
Publisher : John Wiley & Sons
ISBN 13 : 1119654742
Total Pages : 544 pages
Book Rating : 4.1/5 (196 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning and Big Data by : Uma N. Dulhare

Download or read book Machine Learning and Big Data written by Uma N. Dulhare and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning: Convergence to Big Data Analytics

Author : Murad Khan
Publisher : Springer
ISBN 13 : 9811334595
Total Pages : 79 pages
Book Rating : 4.8/5 (113 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan

Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Artificial Intelligence for Big Data

Author : Anand Deshpande
Publisher : Packt Publishing Ltd
ISBN 13 : 1788476018
Total Pages : 371 pages
Book Rating : 4.7/5 (884 download)

DOWNLOAD NOW!

Book Synopsis Artificial Intelligence for Big Data by : Anand Deshpande

Download or read book Artificial Intelligence for Big Data written by Anand Deshpande and published by Packt Publishing Ltd. This book was released on 2018-05-22 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

Demystifying Big Data and Machine Learning for Healthcare

Author : Prashant Natarajan
Publisher : CRC Press
ISBN 13 : 1315389304
Total Pages : 233 pages
Book Rating : 4.3/5 (153 download)

DOWNLOAD NOW!

Book Synopsis Demystifying Big Data and Machine Learning for Healthcare by : Prashant Natarajan

Download or read book Demystifying Big Data and Machine Learning for Healthcare written by Prashant Natarajan and published by CRC Press. This book was released on 2017-02-15 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Big Data, Data Mining, and Machine Learning

Author : Jared Dean
Publisher : John Wiley & Sons
ISBN 13 : 1118920708
Total Pages : 288 pages
Book Rating : 4.1/5 (189 download)

DOWNLOAD NOW!

Book Synopsis Big Data, Data Mining, and Machine Learning by : Jared Dean

Download or read book Big Data, Data Mining, and Machine Learning written by Jared Dean and published by John Wiley & Sons. This book was released on 2014-05-07 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Machine Learning for Big Data Analysis

Author : Siddhartha Bhattacharyya
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110550776
Total Pages : 246 pages
Book Rating : 4.1/5 (15 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning for Big Data Analysis by : Siddhartha Bhattacharyya

Download or read book Machine Learning for Big Data Analysis written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2018-12-17 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent research.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author : Thomas, J. Joshua
Publisher : IGI Global
ISBN 13 : 1799811948
Total Pages : 355 pages
Book Rating : 4.7/5 (998 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning Techniques and Optimization Strategies in Big Data Analytics by : Thomas, J. Joshua

Download or read book Deep Learning Techniques and Optimization Strategies in Big Data Analytics written by Thomas, J. Joshua and published by IGI Global. This book was released on 2019-11-29 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Data Analysis, Machine Learning and Applications

Author : Christine Preisach
Publisher : Springer Science & Business Media
ISBN 13 : 354078246X
Total Pages : 719 pages
Book Rating : 4.5/5 (47 download)

DOWNLOAD NOW!

Book Synopsis Data Analysis, Machine Learning and Applications by : Christine Preisach

Download or read book Data Analysis, Machine Learning and Applications written by Christine Preisach and published by Springer Science & Business Media. This book was released on 2008-04-13 with total page 719 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)

Download Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) PDF Online Free

Author : Rajiv Misra
Publisher : Springer Nature
ISBN 13 : 3030824691
Total Pages : 362 pages
Book Rating : 4.0/5 (38 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) by : Rajiv Misra

Download or read book Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) written by Rajiv Misra and published by Springer Nature. This book was released on 2021-09-29 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.

Big Data Analytics with Java

Author : Rajat Mehta
Publisher : Packt Publishing Ltd
ISBN 13 : 1787282198
Total Pages : 418 pages
Book Rating : 4.7/5 (872 download)

DOWNLOAD NOW!

Book Synopsis Big Data Analytics with Java by : Rajat Mehta

Download or read book Big Data Analytics with Java written by Rajat Mehta and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.

Big Data and Machine Learning in Quantitative Investment

Author : Tony Guida
Publisher : John Wiley & Sons
ISBN 13 : 1119522196
Total Pages : 308 pages
Book Rating : 4.1/5 (195 download)

DOWNLOAD NOW!

Book Synopsis Big Data and Machine Learning in Quantitative Investment by : Tony Guida

Download or read book Big Data and Machine Learning in Quantitative Investment written by Tony Guida and published by John Wiley & Sons. This book was released on 2019-03-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.