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Deep Learning For Data Analytics

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Deep Learning for Data Analytics

Deep Learning for Data Analytics Book
Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
Publisher : Academic Press
Release : 2020-05-29
ISBN : 0128226080
Language : En, Es, Fr & De

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Book Description :

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

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book
Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Publisher : IGI Global
Release : 2019-11-29
ISBN : 1799811948
Language : En, Es, Fr & De

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Book Description :

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.

Fundamentals of Machine Learning for Predictive Data Analytics

Fundamentals of Machine Learning for Predictive Data Analytics Book
Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Release : 2015-07-31
ISBN : 0262331748
Language : En, Es, Fr & De

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Book Description :

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. 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. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

Deep Learning

Deep Learning Book
Author : David Feldspar
Publisher : Createspace Independent Publishing Platform
Release : 2018-02
ISBN : 9781984931498
Language : En, Es, Fr & De

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Book Description :

How can deep learning, even machine learning, help your organization? The lofty expectations about machine learning and deep studies and projects have skyrocketed, and yet, there is so much left to be said about the methods that trigger the higher-functioning corners of the human neural networks. With so many data and investments on the line, how can we deepen our understanding of these subjects? That is where this guide will take you to the next level. It touches on exactly those problems and methods that optimize your financing and comprehension of the little details that often get overlooked. Furthermore, you will read about subtopics like: Popular machine learning methods that are being applied today. Data mining processes that you can easily use for your own company or individual proprietorship. Insights in supervised versus unsupervised data mining. Machine learning tactics and know-how. The five best steps to implement unsupervised big data machine learning. Ten ways to apply predictive analyses to the banking sector. Financial optimization techniques for regular processes. These machine learning, data mining, and other financing strategies are an intellectual, analytical goldmine you can feast your mind on.

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches Book
Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
Publisher : CRC Press
Release : 2020-10-07
ISBN : 1000179516
Language : En, Es, Fr & De

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Book Description :

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Advanced Deep Learning Applications in Big Data Analytics

Advanced Deep Learning Applications in Big Data Analytics Book
Author : Bouarara, Hadj Ahmed
Publisher : IGI Global
Release : 2020-10-16
ISBN : 1799827933
Language : En, Es, Fr & De

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Book Description :

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.

Fundamentals of Machine Learning for Predictive Data Analytics second edition

Fundamentals of Machine Learning for Predictive Data Analytics  second edition Book
Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Release : 2020-10-20
ISBN : 0262361108
Language : En, Es, Fr & De

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Book Description :

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.

Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics Book
Author : Guozhu Dong,Huan Liu
Publisher : CRC Press
Release : 2018-03-14
ISBN : 1351721275
Language : En, Es, Fr & De

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Book Description :

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Machine Learning Cookbook with Python

Machine Learning Cookbook with Python Book
Author : Rehan Guha
Publisher : BPB Publications
Release : 2020-11-12
ISBN : 9389898005
Language : En, Es, Fr & De

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Book Description :

A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects KEY FEATURES Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. DESCRIPTION Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. WHAT WILL YOU LEARN Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. WHO THIS BOOK IS FOR This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. TABLE OF CONTENTS 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI

Learn Data Analytics For Beginners

Learn Data Analytics For Beginners Book
Author : Landon Adrian
Publisher :
Release : 2019-08-11
ISBN : 9781089671534
Language : En, Es, Fr & De

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Book Description :

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming.Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available.

Python Data Analytics

Python Data Analytics Book
Author : tony f. charles
Publisher :
Release : 2019-11-24
ISBN : 9781711094168
Language : En, Es, Fr & De

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Book Description :

Do you want to enter into the new world of Python data science as a beginner in an efficient and effective way? Do you want to master Python programming language basic syntax, functions and library handling techniques with an ultimate guide book? Are you curious to develop data analytics and data science models? Machine learning and Data Science theories provide simple and effective techniques to automate processes and deliver long term advantages to businesses and industries. Python Data Analytics lets readers begin Python from scratch and develop strong skills for developing and implementing machine learning models. Python libraries including Pandas, NumPy, and Scikit-Learn are a major part of the model development process for which complete details and ideas have been shared to make programming simple and effective. Along with Python programming details, the book also includes in-depth explanations regarding machine learning algorithms, deep learning concepts, and data analytics approaches. For effective learning, beginners can overview code samples and acquire sharp Python programming skills to develop reliable, accurate, and high-performing machine learning and deep analytics models. In this book, you will learn: Basics of Python for Data Analysis NumPy 2-D and 3-D arrays SciPy Linear Algebra Pandas Operations Python IDE's Sublme Text Atom Eclipse Basic Syntax Variables and Data Types Decision Making and Basic Operators Object Oriented Programming Regular Expressions Data Handling Load date from different server such as CSV, URL or SQL Python Aggregation Building Machine Learning Models Data Science Data Pipelines Data Segregation Parallelization Importance of Metadata Machine Learning Algorithms Scikit Learn Effective Data Visualization Evaluating Accuracy of the Model Advantages of Naïve Bayes K-Means Clustering Expectation-Minimization Algorithm Mean Shift Algorithm Artificial Neural Networks Keras Deep Neural Networks Architecture of ANN's Data Science in Real World Virtual Assistants Risk and Fraud Detection Data Analytics in Detail Types and Categories of Data Analytics Steps in Data Mining Data Science Lifecycle and Model Building Improving Data Science Models Determine Problems Choosing Hyper Parameters Testing and Evaluation Search for More Data Deep Learning and Business Algorithm Tuning and Method Ensemble Model Interpretability Language Recognition Autonomous Vehicles Finding Useful Data Big Data Organizational Benefits Useful Deep Learning Methods and Techniques Quality Assurance Advanced AI Even if you don't have any experience you can get excellent results In a few days. Scroll up and buy now!

DATA MINING BIG DATA ANALYTICS and DEEP LEARNING with MATLAB

DATA MINING  BIG DATA ANALYTICS and DEEP LEARNING with MATLAB Book
Author : C Perez
Publisher :
Release : 2019-05-24
ISBN : 9781070189048
Language : En, Es, Fr & De

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Book Description :

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In a simple case, there might be two sets of neurons: ones that receive an input signal and ones that send an output signal. When the input layer receives an input it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task (e.g., face recognition or facial expression recognition). One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. Among all these tools highlights MATLAB.

Deep Learning Convergence to Big Data Analytics

Deep Learning  Convergence to Big Data Analytics Book
Author : Murad Khan,Bilal Jan,Haleem Farman
Publisher : Springer
Release : 2018-12-30
ISBN : 9811334595
Language : En, Es, Fr & De

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Book Description :

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.

Big Data Analytics Methods

Big Data Analytics Methods Book
Author : Peter Ghavami
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2019-12-16
ISBN : 1547401567
Language : En, Es, Fr & De

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Book Description :

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Deep Learning Innovations and Their Convergence With Big Data

Deep Learning Innovations and Their Convergence With Big Data Book
Author : Karthik, S.,Paul, Anand,Karthikeyan, N.
Publisher : IGI Global
Release : 2017-07-13
ISBN : 1522530169
Language : En, Es, Fr & De

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Book Description :

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.

Advanced Data Analytics Using Python

Advanced Data Analytics Using Python Book
Author : Sayan Mukhopadhyay
Publisher : Apress
Release : 2018-03-29
ISBN : 1484234502
Language : En, Es, Fr & De

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Book Description :

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.

Fog Computing Deep Learning and Big Data Analytics Research Directions

Fog Computing  Deep Learning and Big Data Analytics Research Directions Book
Author : C.S.R. Prabhu
Publisher : Springer
Release : 2019-01-04
ISBN : 9811332096
Language : En, Es, Fr & De

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Book Description :

This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.

Python for Data Analysis

Python for Data Analysis Book
Author : Daniel Howard
Publisher :
Release : 2019-11-24
ISBN : 9781711119649
Language : En, Es, Fr & De

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Book Description :

Are you searching for a professional guide for learning data analysis from scratch? In this crash course, you will find everything you need to become proficient in this discipline, regardless of your previous experience. Starting from the basics, Daniel Howard will teach you the most up-to-date techniques to manipulate and process data, using Python libraries such us NumPy, Pandas and Matplotlib. Python is one of the most famous programming languages and it is recognized as the most effective tool for machine learning and data science, thanks to its large number of dedicated resources. Since it is designed for everyone, you will be amazed by the large number of programs that you will be able to create in no time, even as a beginner. Here is a preview of what you will learn: What data analysis is, and why it is fundamental in hundreds of business and technological applications Effective computational methods to manipulate large amounts of data Data visualization tools and techniques How to develop a successful career in one of the best paid sectors Real-world applications of machine learning and artificial intelligence What predictive modeling is How to build neural networks with Python If you are a student or a professional looking for more technical skills, or if you are simply curious about data analytics and its powerful applications, then click the BUY button and get your copy!

Machine Learning and Big Data Analytics Paradigms Analysis Applications and Challenges

Machine Learning and Big Data Analytics Paradigms  Analysis  Applications and Challenges Book
Author : Aboul Ella Hassanien,Ashraf Darwish
Publisher : Springer
Release : 2020-12-17
ISBN : 9783030593377
Language : En, Es, Fr & De

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Book Description :

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.