Advanced Analytics And Deep Learning Models

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Advanced Analytics and Deep Learning Models

Author : Archana Mire
Publisher : John Wiley & Sons
ISBN 13 : 111979241X
Total Pages : 436 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Advanced Analytics and Deep Learning Models by : Archana Mire

Download or read book Advanced Analytics and Deep Learning Models written by Archana Mire and published by John Wiley & Sons. This book was released on 2022-05-03 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Advanced Deep Learning Applications in Big Data Analytics

Author : Bouarara, Hadj Ahmed
Publisher : IGI Global
ISBN 13 : 1799827933
Total Pages : 351 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Advanced Deep Learning Applications in Big Data Analytics by : Bouarara, Hadj Ahmed

Download or read book Advanced Deep Learning Applications in Big Data Analytics written by Bouarara, Hadj Ahmed and published by IGI Global. This book was released on 2020-10-16 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262361108
Total Pages : 853 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Author : R. Sujatha
Publisher : CRC Press
ISBN 13 : 1000454533
Total Pages : 216 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by : R. Sujatha

Download or read book Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics written by R. Sujatha and published by CRC Press. This book was released on 2021-09-22 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.

Big Data Analytics Methods

Author : Peter Ghavami
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781530414833
Total Pages : 304 pages
Book Rating : 4.4/5 (148 download)

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Book Synopsis Big Data Analytics Methods by : Peter Ghavami

Download or read book Big Data Analytics Methods written by Peter Ghavami and published by Createspace Independent Publishing Platform. This book was released on 2016-03-06 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 ensemble of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods are covered. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. This book is ideal as a text book for a course or as a reference for data scientists, data engineers, data analysts, Business intelligence practitioners, and business managers. It covers 10 chapters that discuss natural language processing (NLP), data visualization, prediction, optimization, artificial intelligence, regression analysis, cox hazard model and many analytics use case examples with applications in healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services. Big Data Analytics Methods Is a must read for those who wish to gain confidence and knowledge about big data and advanced analytics techniques. Read this book and confidently speak, lead and guide others about machine learning, neural networks, NLP, deep learning, and over 100 other analytics techniques. This book is fun and easy to read. It starts with simple and broad explanation of methods and gradually introduces more technical terms and techniques layer by layer. It finally introduces the underlying mathematical terms for those who want a mathematical foundation of the analytics methods. This book is one of a kind as it provides state of the art in advanced data analytics methods with important best practices to ensure the reader's success in data analytics.

Big Data Analytics Methods

Author : Peter Ghavami
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 1547401583
Total Pages : 282 pages
Book Rating : 4.5/5 (474 download)

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Book Synopsis Big Data Analytics Methods by : Peter Ghavami

Download or read book Big Data Analytics Methods written by Peter Ghavami and published by Walter de Gruyter GmbH & Co KG. This book was released on 2019-12-16 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advanced Analytics and Deep Learning Models

Author : Archana Mire
Publisher : John Wiley & Sons
ISBN 13 : 1119791758
Total Pages : 436 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Advanced Analytics and Deep Learning Models by : Archana Mire

Download or read book Advanced Analytics and Deep Learning Models written by Archana Mire and published by John Wiley & Sons. This book was released on 2022-06-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Deep Learning in Data Analytics

Author : Debi Prasanna Acharjya
Publisher : Springer Nature
ISBN 13 : 3030758559
Total Pages : 271 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Deep Learning in Data Analytics by : Debi Prasanna Acharjya

Download or read book Deep Learning in Data Analytics written by Debi Prasanna Acharjya and published by Springer Nature. This book was released on 2021-08-11 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

No-Code Data Science

Author : David Patrishkoff
Publisher :
ISBN 13 : 9781312022195
Total Pages : 0 pages
Book Rating : 4.0/5 (221 download)

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Book Synopsis No-Code Data Science by : David Patrishkoff

Download or read book No-Code Data Science written by David Patrishkoff and published by . This book was released on 2023-10-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: No-Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first-of-its-kind textbook book is designed to empower readers with the ability to leverage advanced analytics, machine learning, and AI without using a programming language, such as Python or R.It's a comprehensive guide to no-code data science (NCDS) that applies free, no-code, and open-source software with Orange visual programming software, JASP, and BlueSky Statistics. A no-shortcuts approach to ML and AI is applied to maximize the accuracy and application potential of predictive models. The NCDS approach is akin to constructing predictive models with pre-made LEGO bricks (visual programming) versus tediously molding shapes from clay (manual coding). A practical how-to approach to predictive modeling is offered while insisting on the rigor of our disciplined NCDS process. Hands-on data exercises are included in the first eleven chapters. QR code links to educational videos are included in most chapters.Data science background is first explored, discussing basic definitions and data scientist skill sets. This is followed by chapters on data preparation, wrangling, and data visualization. Predictive analytics is covered in chapters on machine learning models and model evaluation. Both supervised and unsupervised learning are included in the discourse. Time series forecasting, survival analysis, and geolocation are covered in separate chapters. Artificial intelligence is featured in chapters on image analysis and text mining. Lastly, the potential impact of machine learning and artificial intelligence on Industry 4.0 is covered in the last chapter. We also offer a pathway for statisticians, Lean Six Sigma practitioners, and other professionals to learn predictive modeling techniques to enable organizations to successfully pursue Industry 4.0 goals.

Data Analytics

Author : Anthony S. Williams
Publisher : Anthony S. Williams
ISBN 13 :
Total Pages : 225 pages
Book Rating : 4./5 ( download)

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Book Synopsis Data Analytics by : Anthony S. Williams

Download or read book Data Analytics written by Anthony S. Williams and published by Anthony S. Williams. This book was released on with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Book Bundle of Data Analytics for Beginners AND Deep Learning with Keras Data Analytics for Beginners: Introduction to Data Analytics Knowing the data generated by your business every day is a key to success in the Data Analytic World that you are competing in. As there is so much data so, the organizations need to collect and store them. The data becomes valuable to businesses when it is analyzed. Prior to the recent rise in analytics, businesses and organizations did not have the capacity to analyze a great deal of data, so a relatively small amount was maintained. In today's data-driven world, anything and everything may have significance, so there has been an attempt to record and keep virtually any data that we have the capacity to collect; and we have a great deal of capacity. There is so much to learn in this bundle about data analytics and I do invite you to grab your copy today and get started! Deep Learning with Keras: Introduction to Deep Learning with Keras This book will introduce you to various deep learning models in Keras, and you will see how different neural networks can be used in real-world examples as well as in various scientific fields. You will explore various Keras algorithms like the simplest linear regression or more complex deep convolutional network. You will get to know what is the difference between supervised and unsupervised deep learning and you will be able to implement various algorithms in Keras by yourself as you follow step-by-step guide in this book. You will explore various applications of deep learning models such as speech recognition systems, natural language processing, and video game development. A whole new world will open in front of you since, by the time you reach the final page of this book, you will be a Keras expert and ready for your deep-learning projects. By downloading this BOOK BUNDLE you will discover... Data Analytics for Beginners: Putting Data Analytics to Work The Rise of Data Analytics Big Data Defined Cluster Analysis Applications of Cluster Analysis Commonly Graphed Information Data Visualization Four Important Features of Data Visualization Software Big Data Impact Envisaged by 2020 Pros and Cons of Big Data Analytics And of course much more! Deep Learning with Keras: Deep Neural Network Neural Network Elements Keras Models Sequential Model Functional API Model Keras Layers Core Keras Layers Convolutional Keras Layers Recurrent Keras Layers Deep Learning Algorithms Supervised Learning Algorithms Applications of Deep Learning Models Automatic Speech and Image Recognition Natural Language Processing Video Game Development Real World Applications And of course much more! Download this BOOK BUNDLE now and SAVE MONEY!!

Advanced Data Analytics Using Python

Author : Sayan Mukhopadhyay
Publisher : Apress
ISBN 13 : 1484234502
Total Pages : 195 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Advanced Data Analytics Using Python by : Sayan Mukhopadhyay

Download or read book Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and published by Apress. This book was released on 2018-03-29 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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)

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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 for Predictive Analysis

Author : Amit Joshi
Publisher : Springer Nature
ISBN 13 : 9811571066
Total Pages : 627 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Machine Learning for Predictive Analysis by : Amit Joshi

Download or read book Machine Learning for Predictive Analysis written by Amit Joshi and published by Springer Nature. This book was released on 2020-10-22 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains, machine learning, intelligent information retrieval and business intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.

Applied Advanced Analytics

Author : Arnab Kumar Laha
Publisher : Springer Nature
ISBN 13 : 9813366567
Total Pages : 236 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Applied Advanced Analytics by : Arnab Kumar Laha

Download or read book Applied Advanced Analytics written by Arnab Kumar Laha and published by Springer Nature. This book was released on 2021-06-08 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.

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)

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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.

Advanced Analytics for Business

Author : Mark Ryan
Publisher : Manning
ISBN 13 : 9781633438545
Total Pages : 0 pages
Book Rating : 4.4/5 (385 download)

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Book Synopsis Advanced Analytics for Business by : Mark Ryan

Download or read book Advanced Analytics for Business written by Mark Ryan and published by Manning. This book was released on 2024-09-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Every organization in the world stores data in tables. Advanced Analytics for Business reveals practical techniques for applying machine learning techniques like deep learning and gradient boosting to your company’s rows and columns. Inside Advanced Analytics for Business you’ll learn how to: Pick the right machine learning approach for your data Apply deep learning to tabular data Deploy tabular machine learning locally and in the cloud Pipelines to automatically train and maintain a model This book collects best practices, hard-won tips and tricks, and hands-on techniques for making sense of tabular data using advanced machine learning techniques. Inside, you’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book Advanced Analytics for Business teaches you to train insightful machine learning models on common tabular data such as spreadsheets, databases, and logs. It covers classic machine learning techniques like gradient boosting and more contemporary deep learning approaches. You’ll find practical examples for every stage of the machine learning pipeline, such as using XGBoost and Keras to predict the prices of Airbnb listings in New York City and deploying a machine learning model on your local system with Flask. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. About the reader For readers experienced with Python and the basics of machine learning. About the author Mark Ryan is a technical writing manager at Google. He studied computer science at the University of Waterloo and at the University of Toronto. In addition to a keen interest in deep learning with tabular data, Mark is interested in applications of large language models. Luca Massaron is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of bestselling books on AI, machine learning, and algorithms.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Author : Om Prakash Jena
Publisher : CRC Press
ISBN 13 : 1000533972
Total Pages : 332 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications by : Om Prakash Jena

Download or read book Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications written by Om Prakash Jena and published by CRC Press. This book was released on 2022-02-25 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.