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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Wor
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US $6,72 (circa EUR 5,75) USPS Media MailTM.
Oggetto che si trova a: Dublin, California, Stati Uniti
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Consegna prevista tra il mer 23 lug e il sab 26 lug a 94104
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Numero oggetto eBay:205600844551
Specifiche dell'oggetto
- Condizione
- Book Title
- Fundamentals of Machine Learning for Predictive Data Analytics: A
- Narrative Type
- Nonfiction
- Genre
- Specialty Boutique
- Topic
- Internet & Social Media
- Intended Audience
- Adult
- Inscribed
- NO
- ISBN
- 9780262029445
Informazioni su questo prodotto
Product Identifiers
Publisher
MIT Press
ISBN-10
0262029448
ISBN-13
9780262029445
eBay Product ID (ePID)
208620163
Product Key Features
Number of Pages
624 Pages
Publication Name
Fundamentals of Machine Learning for Predictive Data Analytics : Algorithms, Worked Examples, and Case Studies
Language
English
Subject
Probability & Statistics / Stochastic Processes, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2015
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
36.5 Oz
Item Length
9.2 in
Item Width
7.3 in
Additional Product Features
Intended Audience
Trade
LCCN
2014-046123
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
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., 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.
LC Classification Number
Q325.5.K455 2015
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nerdssavetheworld
100% di Feedback positivi•348 oggetti venduti
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Feedback sul venditore (126)
- e***n (120)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoVery happy with this purchase! The packaging was great, and the value was well worth the price. Shipping was fast. Just as the listing described.
- s***j (111)- Feedback lasciato dall'acquirente.Ultimo annoAcquisto verificatoThank you again.. love doing business with this seller.. they are awesome & responsive & fast shipping & very fair shipping price. I’ve done business twice & will again hopefully in the future.. they deserve 10 stars but since I can’t then I’ll say A+++++++++++++ Thank you to the seller & to any potential buyers that read this, don’t hesitate bc you are in good hands & they definitely package the items very, very well..Lot Of 21 Issues Mad Magazines From 2002-2003 Various Condition Britney Potter (N° 204986666140)
- 4***r (33)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoItems arrived quickly and safely. Great packaging to protect them. Items were in good condition as described. Happy with the purchase. Thank you!