Abstract
Data science has gained importance since available data and hardware facilities have been ubiquitous. Algorithms to process a huge amount of data and extract information were developed decades ago. However, due to the lack of high-capacity computers, it was not possible to use them on real-life data and problems. Today, from finance to medicine data science plays an important role to solve problems. Suffice it to say, machine learning algorithms are the core of this new phenomenon besides data itself. Artificial neural networks, deep learning, Support Vector Machines, Decision Tree Learning Models, and related algorithms have been used successfully and yielded very important results recently. On the other hand, text data have also gained importance being the fuel of machine learning in data science. Especially the emergence of social media and communication technology contributed to the popularity of texts in data science. In this chapter, concise introductions have been given about the most popular and also successful machine learning algorithms. This chapter will be helpful for those readers who do not have enough information about machine learning and its algorithms.
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Appendix
Appendix
Key Terms and Definitions
- NAG::
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Nesterov Accelerated Gradient
- SVM::
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Support Vector Machine
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Silahtaroğlu, G., Dinçer, H., Yüksel, S. (2021). Introduction to Data Science and Machine Learning Algorithms. In: Data Science and Multiple Criteria Decision Making Approaches in Finance. Multiple Criteria Decision Making. Springer, Cham. https://doi.org/10.1007/978-3-030-74176-1_1
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DOI: https://doi.org/10.1007/978-3-030-74176-1_1
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