A Survey on Essentials of Machine Learning Algorithms with Python and R codes
Keywords:
Python, Machine Learning, Programming with R, Decision tree, etcAbstract
Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves. We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to cloud. What makes this period exciting for someone is the democratization of the tools and techniques, which followed the boost in computing. Today, as a data scientist, people can build data crunching machines with complex algorithms for a few dollors per hour.
References
[1] http://www.britannica.com/EBchecked/topic/1116194/machine-learning This tertiary source reuses information from other sources but does not name them.
[2] [Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274.
[3] Machine learning and pattern recognition "can be viewed as two facets of the same field."
[4] https://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/
[5] Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, IEEE Signal Processing Magazine, vol. 27, no. 4, July 2010, pp. 25–38.
[6] Mannila, Heikki (1996). Data mining: machine learning, statistics, and databases. Int'l Conf. Scientific and Statistical Database Management. IEEE Computer Society.
[7] Friedman, Jerome H. (1998). "Data Mining and Statistics: What's the connection?". Computing Science and Statistics. 29 (1): 3–9.
[8] "Machine Learning: What it is and why it matters". www.sas.com. Retrieved 2016-03-29.
[9] Harnad, Stevan (2008), "The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence", in Epstein, Robert; Peters, Grace, The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer, Kluwer
[10] Russell, Stuart; Norvig, Peter (2003) [1995]. Artificial Intelligence: A Modern Approach (2nd ed.). Prentice Hall. ISBN 978-0137903955.
[11] Langley, Pat (2011). "The changing science of machine learning". Machine Learning. 82 (3): 275–279. doi:10.1007/s10994-011-5242-y.
[12] Le Roux, Nicolas; Bengio, Yoshua; Fitzgibbon, Andrew (2012). "Improving First and Second-Order Methods by Modeling Uncertainty". In Sra, Suvrit; Nowozin, Sebastian; Wright, Stephen J. Optimization for Machine Learning. MIT Press. p. 404.
[13] Alpaydin, Ethem (2010). Introduction to Machine Learning. London: The MIT Press. ISBN 978-0-262-01243-0. Retrieved 4 February 2017.
[14] Cornell University Library. "Breiman: Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)". Retrieved 8 August 2015.
[15] Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. p. vii.
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