Abstract
Nowadays machine learning (ML) is used in all sorts of fields like health care, retail, travel, finance, social media, etc. ML system is used to learn from input data to construct a suitable model by continuously estimating, optimizing, and tuning parameters of the model. To attain the stated, Python programming language is one of the most flexible languages, and it does contain special libraries for ML applications, namely SciKit-Learn, TensorFlow, PyTorch, Keras, Theano, etc., which is great for linear algebra and getting to know kernel methods of machine learning. The Python programming language is great to use when working with ML algorithms and has easy syntax relatively. When taking the deep-dive into ML, choosing a framework can be daunting. The most common concern is to understand which of these libraries has the most momentum in ML system modeling and development. The major objective of this paper is to provide extensive knowledge on various Python libraries and different ML algorithms in comparison with meet multiple application requirements. This paper also reviewed various ML algorithms and application domains.
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Rajesh, M., Sheshikala, M. (2023). Top Five Machine Learning Libraries in Python: A Comparative Analysis. In: Bhateja, V., Sunitha, K.V.N., Chen, YW., Zhang, YD. (eds) Intelligent System Design. Lecture Notes in Networks and Systems, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-19-4863-3_55
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