Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making, Hemachandran K, Sayantan Khanra, Raul V. Rodriguez

Автор: Subasi, Abdulhamit
Название: Practical Machine Learning For Data Analysis Using Python
ISBN: 0128213795 ISBN-13(EAN): 9780128213797
Издательство: Elsevier Science
Рейтинг:
Цена: 110030.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.

Автор: Sarker Iqbal, Colman Alan, Han Jun
Название: Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making
ISBN: 3030885291 ISBN-13(EAN): 9783030885298
Издательство: Springer
Рейтинг:
Цена: 130430.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making.

Автор: O`Neil Bonnie K., Fryman Lowell
Название: The Data Catalog: Sherlock Holmes Data Sleuthing for Analytics
ISBN: 1634627873 ISBN-13(EAN): 9781634627870
Издательство: Gazelle Book Services
Рейтинг:
Цена: 57190.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight. The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise. Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs. ONeil & Frymanpresent a typology which ranges from products that focus on data lineage, curation and search, data governance, data preparation, and of course, the core capability of finding and understanding the data. The authors emphasize that machine learning is being adopted in many of these products, enabling a more elegant data democratization solution in the face of the burgeoning mountain of data that is engulfing organizations. Derek Strauss, Chairman/CEO, Gavroshe, and Former CDO, TD Ameritrade. This book is organized into three sections: Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog; Chapters 3-10 present the many different types of data catalogs; Chapters 11 and 12 provide an extensive features list, current trends, and visions for the future.

Автор: Hassanien Aboul Ella, Darwish Ashraf
Название: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
ISBN: 3030593371 ISBN-13(EAN): 9783030593377
Издательство: Springer
Цена: 186330.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: Hassanien Aboul Ella, Darwish Ashraf
Название: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
ISBN: 3030593401 ISBN-13(EAN): 9783030593407
Издательство: Springer
Рейтинг:
Цена: 186330.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: Guo, Song (the Hong Kong Polytechnic University) Qu, Zhihao (the Hong Kong Polytechnic University)
Название: Edge learning for distributed big data analytics
ISBN: 1108832377 ISBN-13(EAN): 9781108832373
Издательство: Cambridge Academ
Рейтинг:
Цена: 63350.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential reading for experienced researchers and developers, or for those who are just entering the field.

Автор: Papp Stefan, Weidinger Wolfgang, Munro Katherine
Название: The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics
ISBN: 1569908869 ISBN-13(EAN): 9781569908860
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 50150.00 T
Наличие на складе: Нет в наличии.
Описание: Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring:- A comprehensive overview of the various fields of application of data science- Case studies from practice to make the described concepts tangible- Practical examples to help you carry out simple data analysis projectsThe book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. Contains these current issues:- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies- Computer vision: How can we gain insights from images and videos with data science?- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.- ML and AI in production: How to turn experimentation into a working data science product?- Presenting your results: Essential presentation techniques for data scientistsContributors: Stefan Papp / Wolfgang Weidinger / Katherine Munro / Bernhard Ortner / Annalisa Cadonna / Georg Langs / Roxane Licandro / Mario Meir-Huber / Danko Nikoli? / Zoltan Toth / Barbora Vesela / Rania Wazir / G?nther Zauner

Автор: Editors: Deo, R., Samui, P., Kisi, O., Zaher, Y.
Название: Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation
ISBN: 9811557713 ISBN-13(EAN): 9789811557712
Издательство: Springer
Рейтинг:
Цена: 167700.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables.

Автор: Xia Lirong
Название: Learning and Decision-Making from Rank Data
ISBN: 1681734400 ISBN-13(EAN): 9781681734408
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 61910.00 T
Наличие на складе: Невозможна поставка.
Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Автор: Xia Lirong
Название: Learning and Decision-Making from Rank Data
ISBN: 1681734427 ISBN-13(EAN): 9781681734422
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 82230.00 T
Наличие на складе: Невозможна поставка.
Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Автор: Nokeri Tshepo Chris
Название: Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps
ISBN: 1484277821 ISBN-13(EAN): 9781484277829
Издательство: Springer
Рейтинг:
Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: Static 2D and 3D GraphsChapter Goal: This chapter introduces the basics of tabulating data and constructing staticgraphical representations. To begin with, it exhibits an approach of extracting and tabulating data by implementing the pandas and sqlalchemy library. Subsequently, it reveals a prevalent 2D and 3D charting recognized as Matplotlib, then exhibits a technique of constructing basic charts (i.e. box-whisker plot, histogram, line plot, and scatter plot).● Tabulating Data● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Plot● 3D Charting● Conclusion
Chapter 2: Interactive ChartingChapter Goal: This chapter introduces an approach for constructing interactive charts byimplementing the most prevalent library, recognized as Plotly.● Plotly● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Ploto Bar Charto Pie Charto Sunburst● 3D Charting● Conclusion
Chapter 3: Containing functionality in Interactive GraphsChapter Goal: This chapter extends to the preceding chapter. It introduces an approach toupdating interactive graphs to improve user experience. For instance, you will learn how to add buttons and range sliders, among other functionalities. Besides that, it exhibits an approach for integrating innumerable graphs into one graph with some functionality.● Updating Graph Layout● Updating Plotly Axes● Including Range Slider● Including Buttons to a Graph● Styling Interactive Graphs● Updating Plotly X-Axis● Color Sequencing● Subplots● Conclusions
Chapter 4: Essentials of HTMLChapter Goal: This chapter introduces the most prevalent markup language for developingwebsites. It acquaints you with the essentials of designing websites. Besides that, it contains a richset of code and examples to support you in getting started with coding using HTML.● The Communication between a Web Browser and Web Server● Domain Hostingo Shared Hostingo Managed Hosting● HyperText Markup Languageo HTML Elements▪ Headings▪ Paragraphs▪ Div▪ Span▪ Buttons▪ Text Box▪ Input▪ File Upload▪ Label▪ Form▪ Meta Tag● Practical Example● Conclusion
Chapter 5: Python Web Frameworks and ApplicationsChapter Goal: The preceding chapter acquainted you with interactive visualization using Plotly. This chapter introduces key Python web frameworks (i.e., flask and dash) and how they differ.Besides that, it provides practical examples and helps you get started with Python web development.● Web Frameworks● Web Applications● Flasko WSGIo Werkzeugo Jinjao Installing Flasko Initializing a Flask Web Applicationo Flask Application Codeo Deploy a Flask Web Application● Dasho Installing Dash Dependencieso Initializing a Dash Web Applicationo Dash Application Codeo Deploy a Dash Web Application● Jupyter Dash● Conclusion
Chapter 6: Dash Bootstrap ComponentsChapter Goal: This chapter covers dash_bootstrap_component. It is a Python library from the Plotly family, which enables us to have key bootstrap func

Автор: Steven L. Brunton, J. Nathan Kutz
Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
ISBN: 1108422098 ISBN-13(EAN): 9781108422093
Издательство: Amazon Internet
Рейтинг:
Цена: 0.00 T
Наличие на складе: Невозможна поставка.
Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.