Download Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) PDF EPUB
Author: Author
Pages: 624
Size: 3.567,74 Kb
Publication Date: July 24,2015
Category: Artificial Intelligence
A comprehensive intro to the most crucial machine learning approaches found in predictive data analytics, covering both theoretical principles and practical applications. Complex and mathematical material is normally augmented with explanatory proved helpful examples, and case research illustrate the use of these versions in the broader business context. These versions are found in predictive data analytics applications which includes price prediction, risk evaluation, predicting consumer behavior, and record classification. This introductory textbook gives a detailed and concentrated treatment of the most crucial machine learning approaches found in predictive data analytics, covering both theoretical principles and useful applications.
Machine learning is frequently utilized to build predictive versions by extracting patterns from huge datasets.
After talking about the trajectory from data to insight to decision, the reserve describes four methods to machine learning: information-centered learning, similarity-structured learning, probability-structured learning, and error-structured learning. Finally, the book considers approaches for evaluating prediction versions and will be offering two case research that describe particular data analytics tasks through each stage of advancement, from formulating the business enterprise problem to execution of the analytics option. Each of these techniques is launched by a nontechnical description of the underlying idea, followed by mathematical versions and algorithms illustrated by comprehensive worked examples. The publication, knowledgeable by the authors’ a long time of teaching machine learning, and focusing on predictive data analytics tasks, is suitable for make use of by undergraduates in pc technology, engineering, mathematics, or figures; by graduate learners in disciplines with applications for predictive data analytics;
and as a reference for specialists.See also
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