Fundamentals of Machine Learning for Predictive Data Analytics, second edition (ePub)
Algorithms, Worked Examples, and Case Studies
(Sprache: Englisch)
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....
Machine learning is often used to build predictive models by extracting patterns from large datasets....
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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.
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.
Autoren-Porträt von John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
• Prof. John D. Kelleher, Academic Leader of the Information, Communication and Entertainment Research Institute, Technological University Dublin (Ireland)• Brian MacNamee, Lecturer, School of Computer Science, University College Dublin
• Aoife D'Arcy, Managing Director, The Analytics Store, Dublin, Ireland
Bibliographische Angaben
- Autoren: John D. Kelleher , Brian Mac Namee , Aoife D'Arcy
- 2020, 856 Seiten, Englisch
- Verlag: MIT Press
- ISBN-10: 0262361108
- ISBN-13: 9780262361101
- Erscheinungsdatum: 20.10.2020
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Dateiformat: ePub
- Größe: 43 MB
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Sprache:
Englisch
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