Abstract
In Chap. 1, we presented a total overview of Big Data Analytics. In this chapter, we delve deeper into Machine Learning and Intelligent Systems. By definition, an algorithm is a sequence of steps in a computer program that transforms given input into desired output. Machine learning is the study of artificially intelligent algorithms that improve their performance at some task with experience. With the availability of big data, machine learning is becoming an integral part of various computer systems. In such systems, the data analyst has access to sample data and would like to construct a hypothesis on the data. Typically, a hypothesis is chosen from a set of candidate patterns assumed in the data. A pattern is taken to be the algorithmic output obtained from transforming the raw input. Thus, machine learning paradigms try to build general patterns from known data to make predictions on unknown data.
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Prabhu, C., Chivukula, A., Mogadala, A., Ghosh, R., Livingston, L. (2019). Intelligent Systems. In: Big Data Analytics: Systems, Algorithms, Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-0094-7_2
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DOI: https://doi.org/10.1007/978-981-15-0094-7_2
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