Machine Learning
Rajendra Akerkar
Additional contact information
Rajendra Akerkar: Western Norway Research Institute
A chapter in Artificial Intelligence for Business, 2019, pp 19-32 from Springer
Abstract:
Abstract This chapter discusses core machine learning – workflow and the most effective machine learning techniques. Machine learning is the process of teaching a computer system how to make accurate predictions when fed data. After a brief overview of the discipline’s most common techniques and applications, readers will gain more insight into the assessment and training of different machine learning models for business problems.
Keywords: Machine Learning Workflow; Gradient Boosting; Supervised Machine Learning Problem; Goal-seeking Agents; Reinforcement Learning (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:spbrcp:978-3-319-97436-1_2
Ordering information: This item can be ordered from
http://www.springer.com/9783319974361
DOI: 10.1007/978-3-319-97436-1_2
Access Statistics for this chapter
More chapters in SpringerBriefs in Business from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().