Abstract
The world is quickly adapting the use of machine learning (ML). Whether its driverless cars, the intelligent personal assistant, or machines playing the games like Go and Jeopardy against humans, ML is pervasive. The availability and ease of collecting data coupled with high computing power has made this field even more conducive to researchers and businesses to explore data-driven solutions for some of the most challenging problems. This has led to a revolution and outbreak in the number of new startups and tools leveraging ML to solve problems in sectors such as healthcare, IT, HR, automobiles, manufacturing, and the list is ever expanding.
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Notes
- 1.
Chapelle O, SchÖlkopf B, Zien A (eds.) (2006). Semi-Supervised Learning. MIT Press, Cambridge, MA.
- 2.
Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall Press, Upper Saddle River, NJ, USA.
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© 2019 Karthik Ramasubramanian and Abhishek Singh
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Ramasubramanian, K., Singh, A. (2019). Machine Learning Theory and Practice. In: Machine Learning Using R. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4215-5_6
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DOI: https://doi.org/10.1007/978-1-4842-4215-5_6
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