@article{(Open Science Index):https://publications.waset.org/pdf/10012071,
	  title     = {Comparative Analysis of Machine Learning Tools: A Review},
	  author    = {S. Sarumathi and  M. Vaishnavi and  S. Geetha and  P. Ranjetha},
	  country	= {},
	  institution	= {},
	  abstract     = {Machine learning is a new and exciting area of
artificial intelligence nowadays. Machine learning is the most
valuable, time, supervised, and cost-effective approach. It is not a
narrow learning approach; it also includes a wide range of methods
and techniques that can be applied to a wide range of complex realworld
problems and time domains. Biological image classification,
adaptive testing, computer vision, natural language processing, object
detection, cancer detection, face recognition, handwriting
recognition, speech recognition, and many other applications of
machine learning are widely used in research, industry, and
government. Every day, more data are generated, and conventional
machine learning techniques are becoming obsolete as users move to
distributed and real-time operations. By providing fundamental
knowledge of machine learning tools and research opportunities in
the field, the aim of this article is to serve as both a comprehensive
overview and a guide. A diverse set of machine learning resources is
demonstrated and contrasted with the key features in this survey.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {6},
	  year      = {2021},
	  pages     = {354 - 363},
	  ee        = {https://publications.waset.org/pdf/10012071},
	  url   	= {https://publications.waset.org/vol/174},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 174, 2021},
	}