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

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Publisher : Springer Nature
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ISBN 10 : 9783030593384
Pages : 648 pages
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Rating : 4.9/5 (593 users download)


Download Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges by Aboul Ella Hassanien PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges by Aboul Ella Hassanien. This book is available in ePub and PDF format with a single click unlimited downloads. 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.


Data Analysis, Machine Learning and Applications

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Publisher : Springer Science & Business Media
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ISBN 10 : 9783540782469
Pages : 719 pages
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Rating : 4.8/5 (782 users download)


Download Data Analysis, Machine Learning and Applications by Christine Preisach PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Analysis, Machine Learning and Applications by Christine Preisach. This book is available in ePub and PDF format with a single click unlimited downloads. 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.


Deep Learning in Data Analytics

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Publisher : Springer
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ISBN 10 : 3030758540
Pages : 266 pages
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Rating : 4.3/5 (3 users download)


Download Deep Learning in Data Analytics by Debi Prasanna Acharjya PDF/Ebook Free clicking on the below button will initiate the downloading process of Deep Learning in Data Analytics by Debi Prasanna Acharjya. This book is available in ePub and PDF format with a single click unlimited downloads. This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.


Advanced Deep Learning Applications in Big Data Analytics

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Publisher : IGI Global
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ISBN 10 : 9781799827931
Pages : 351 pages
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Rating : 4.2/5 (827 users download)


Download Advanced Deep Learning Applications in Big Data Analytics by Bouarara, Hadj Ahmed PDF/Ebook Free clicking on the below button will initiate the downloading process of Advanced Deep Learning Applications in Big Data Analytics by Bouarara, Hadj Ahmed. This book is available in ePub and PDF format with a single click unlimited downloads. Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.


Big Data Analysis and Deep Learning Applications

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Publisher : Springer
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ISBN 10 : 9789811308697
Pages : 386 pages
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Rating : 4.0/5 (38 users download)


Download Big Data Analysis and Deep Learning Applications by Thi Thi Zin PDF/Ebook Free clicking on the below button will initiate the downloading process of Big Data Analysis and Deep Learning Applications by Thi Thi Zin. This book is available in ePub and PDF format with a single click unlimited downloads. This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.


Deep Learning for Data Analytics

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Publisher : Academic Press
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ISBN 10 : 9780128226087
Pages : 218 pages
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Rating : 4.2/5 (226 users download)


Download Deep Learning for Data Analytics by Himansu Das PDF/Ebook Free clicking on the below button will initiate the downloading process of Deep Learning for Data Analytics by Himansu Das. This book is available in ePub and PDF format with a single click unlimited downloads. Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning


Data analysis, machine learning, and applications

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Publisher :
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ISBN 10 : OCLC:441857563
Pages : 227 pages
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Rating : 4./5 ( users download)


Download Data analysis, machine learning, and applications by PDF/Ebook Free clicking on the below button will initiate the downloading process of Data analysis, machine learning, and applications by . This book is available in ePub and PDF format with a single click unlimited downloads.


Machine Learning and Data Science

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Publisher : Wiley-Scrivener
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ISBN 10 : 1119775612
Pages : 467 pages
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Rating : 4.1/5 (119 users download)


Download Machine Learning and Data Science by Prateek Agrawal PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning and Data Science by Prateek Agrawal. This book is available in ePub and PDF format with a single click unlimited downloads. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The outstanding new volume focuses on the latest developments in machine learning and data science, as well as on the synergy between data science and machine learning. This book explores new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. The book encompasses all aspects of research and development in ML and DS, including but not limited to data discovery, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, bioinformatics and their applications in the areas of engineering, business and social sciences. It covers a broad spectrum of applications in the community, from industry, government, and academia. Whether for the veteran engineer or scientist, the student, or a manager or other technician working in the field, this volume is a must-have for any library.


ICDSMLA 2019

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Publisher : Springer Nature
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ISBN 10 : 9789811514203
Pages : 1988 pages
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Rating : 4.1/5 (514 users download)


Download ICDSMLA 2019 by Amit Kumar PDF/Ebook Free clicking on the below button will initiate the downloading process of ICDSMLA 2019 by Amit Kumar. This book is available in ePub and PDF format with a single click unlimited downloads. This book gathers selected high-impact articles from the 1st International Conference on Data Science, Machine Learning & Applications 2019. It highlights the latest developments in the areas of Artificial Intelligence, Machine Learning, Soft Computing, Human–Computer Interaction and various data science & machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.


Fundamentals of Machine Learning for Predictive Data Analytics, second edition

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Publisher : MIT Press
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ISBN 10 : 9780262044691
Pages : 856 pages
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Rating : 4.4/5 (44 users download)


Download Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher PDF/Ebook Free clicking on the below button will initiate the downloading process of Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher. This book is available in ePub and PDF format with a single click unlimited downloads. 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. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.


Data Science for Marketing Analytics

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781800563889
Pages : 636 pages
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Rating : 4.6/5 (563 users download)


Download Data Science for Marketing Analytics by Mirza Rahim Baig PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Science for Marketing Analytics by Mirza Rahim Baig. This book is available in ePub and PDF format with a single click unlimited downloads. This book on marketing analytics with Python will quickly get you up and running using practical data science and machine learning to improve your approach to marketing. You'll learn how to analyze sales, understand customer data, predict outcomes, and present conclusions with clear visualizations.


Data Analysis, Machine Learning and Applications

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ISBN 10 : 354084905X
Pages : 740 pages
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Rating : 4.4/5 (54 users download)


Download Data Analysis, Machine Learning and Applications by Christine Preisach PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Analysis, Machine Learning and Applications by Christine Preisach. This book is available in ePub and PDF format with a single click unlimited downloads.


Machine Learning: End-to-End guide for Java developers

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781788629409
Pages : 1159 pages
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Rating : 4.2/5 (629 users download)


Download Machine Learning: End-to-End guide for Java developers by Richard M. Reese PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning: End-to-End guide for Java developers by Richard M. Reese. This book is available in ePub and PDF format with a single click unlimited downloads. Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming About This Book Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-cases Who This Book Is For This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. What You Will Learn Understand key data analysis techniques centered around machine learning Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more In Detail Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning. The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books: Java for Data Science Machine Learning in Java Mastering Java Machine Learning On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence. Style and approach This comprehensive course proceeds from being a tutorial to a practical guide, providing an introduction to machine learning and different machine learning techniques, exploring machine learning with Java libraries, and demonstrating real-world machine learning use cases using the Java platform.


Data Mining and Machine Learning Applications

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Publisher : Wiley-Scrivener
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ISBN 10 : 1119791782
Pages : 496 pages
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Rating : 4.1/5 (119 users download)


Download Data Mining and Machine Learning Applications by Rohit Raja PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Mining and Machine Learning Applications by Rohit Raja. This book is available in ePub and PDF format with a single click unlimited downloads. DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.


Data Analysis, Machine Learning and Knowledge Discovery

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Publisher : Springer Science & Business Media
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ISBN 10 : 9783319015958
Pages : 470 pages
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Rating : 4.1/5 (15 users download)


Download Data Analysis, Machine Learning and Knowledge Discovery by Myra Spiliopoulou PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Analysis, Machine Learning and Knowledge Discovery by Myra Spiliopoulou. This book is available in ePub and PDF format with a single click unlimited downloads. Data analysis, machine learning and knowledge discovery 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, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012. ​


Beginning Mathematica and Wolfram for Data Science

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Publisher : Apress
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ISBN 10 : 1484265939
Pages : pages
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Rating : 4.8/5 (484 users download)


Download Beginning Mathematica and Wolfram for Data Science by Jalil Villalobos Alva PDF/Ebook Free clicking on the below button will initiate the downloading process of Beginning Mathematica and Wolfram for Data Science by Jalil Villalobos Alva. This book is available in ePub and PDF format with a single click unlimited downloads. Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book introduces you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering The fundamentals of the Wolfram Neural Network Framework and how to build your neural network for different tasks How to use pre-trained models from the Wolfram Neural Net Repository Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.


Data Analytics in Biomedical Engineering and Healthcare

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Publisher : Academic Press
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ISBN 10 : 9780128193150
Pages : 292 pages
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Rating : 4.9/5 (193 users download)


Download Data Analytics in Biomedical Engineering and Healthcare by Kun Chang Lee PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Analytics in Biomedical Engineering and Healthcare by Kun Chang Lee. This book is available in ePub and PDF format with a single click unlimited downloads. Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks


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