Fundamentals Of Machine Learning For Predictive Data Analytics

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Fundamentals Of Machine Learning For Predictive Data Analytics

Fundamentals Of Machine Learning For Predictive Data Analytics
Author: John D. Kelleher
Publisher: MIT Press
ISBN: 0262331748
Size: 80.30 MB
Format: PDF, Mobi
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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.
Fundamentals of Machine Learning for Predictive Data Analytics
Language: en
Pages: 624
Authors: John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
Categories: Computers
Type: BOOK - Published: 2015-07-31 - Publisher: MIT Press
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
Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Language: en
Pages: 856
Authors: John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
Categories: Computers
Type: BOOK - Published: 2020-10-20 - Publisher: MIT Press
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,
Життя 3.0. Доба штучного інтелекту
Language: uk
Pages: 432
Authors: Макс Теґмарк
Categories: Technology & Engineering
Type: BOOK - Published: 2019-07-12 - Publisher: Наш формат
Зa чac cвoгo icнувaння життя нa Зeмлi тpaнcфopмoвувaлocя кiлькa paзiв — вiд бaктepiй, чий poзвитoк пoвнicтю зaкoдoвaний у ДНК, дo cклaднiшиx opгaнiзмiв i зpeштoю людини, якa мaє вибip тa мaйжe миттєвo пpиcтocoвуєтьcя дo нoвoгo cepeдoвищa. Ця гнучкicть дaлa змoгу людcтву зaпaнувaти нa Зeмлi. Тa пoпpи знaчнi пpopиви — мoву, пиceмнicть,
Fundamentals of Predictive Text Mining
Language: en
Pages: 239
Authors: Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Categories: Computers
Type: BOOK - Published: 2015-09-07 - Publisher: Springer
This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This
Data Science Fundamentals Part 2
Language: en
Pages:
Authors: Jonathan Dinu
Categories: Computers
Type: BOOK - Published: 2019 - Publisher:
21+ Hours of Video Instruction Data Science Fundamentals Part II teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty
Big Data Analytics for Cyber-Physical Systems
Language: en
Pages: 360
Authors: Guido Dartmann, Houbing Song, Anke Schmeink
Categories: Law
Type: BOOK - Published: 2019-07-15 - Publisher: Elsevier
Cyber-physical systems (CPS) and the Internet of Things (IoT) are rapidly developing technologies that are transforming our society. The disruptive transformation of the economy and society is expected due to the data collected by these systems, rather than the technological aspects of such as networks, embedded systems, and cloud technology.
R Machine Learning By Example
Language: en
Pages: 340
Authors: Raghav Bali, Dipanjan Sarkar
Categories: Computers
Type: BOOK - Published: 2016-03-31 - Publisher: Packt Publishing Ltd
Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book Get to grips with the concepts of machine learning through exciting real-world examples Visualize and solve complex problems by using power-packed R constructs and its robust packages
Data Science Fundamentals Part 1
Language: en
Pages:
Authors: Jonathan Dinu
Categories: Computers
Type: BOOK - Published: 2017 - Publisher:
20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with
Statistics for Machine Learning
Language: en
Pages: 442
Authors: Pratap Dangeti
Categories: Computers
Type: BOOK - Published: 2017-07-21 - Publisher: Packt Publishing Ltd
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this
Applied Machine Learning
Language: en
Pages: 656
Authors: M GOPAL
Categories: Technology & Engineering
Type: BOOK - Published: 2019-06-07 - Publisher: McGraw Hill Professional
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed