International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 129

Research Paper | Computer Science & Engineering | India | Volume 5 Issue 10, October 2016


Study on the Fundamentals of Machine Learning Approach

Zeinab Samdaliri


Abstract: Now Days Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are being utilized into the predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, & document classification. This introductory research offers a detailed & focused treatment of the most vital machine learning approaches is being used into the predictive data analytics, covering both theoretical concepts & practical applications. Technical & mathematical material is augmented with explanatory worked examples, & case studies illustrate the fundamentals of these models into the broader business context. After discussing the trajectory from data to insight to decision, the research describes four approaches to machine learning information-based learning, similarity based of learning, probability based of learning, & error-based learning. Each of these approaches is introduced by the nontechnical details of the given concept. Finally, the study considers techniques for evaluating prediction models & offers two types of the research that define the specific data analytics projects through each phase of development, from the formulating the corporate issues to the implementation of analytics solution.


Keywords: Machine learning, Fundamentals, analytical study, SVM, neural network


Edition: Volume 5 Issue 10, October 2016,


Pages: 991 - 996

Study on the Fundamentals of Machine Learning Approach


How to Cite this Article?

Zeinab Samdaliri, "Study on the Fundamentals of Machine Learning Approach", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=13101602, Volume 5 Issue 10, October 2016, 991 - 996, #ijsrnet

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