TY - JFULL AU - S. Sarumathi and M. Vaishnavi and S. Geetha and P. Ranjetha PY - 2021/7/ TI - Comparative Analysis of Machine Learning Tools: A Review T2 - International Journal of Computer and Information Engineering SP - 353 EP - 363 VL - 15 SN - 1307-6892 UR - https://publications.waset.org/pdf/10012071 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 174, 2021 N2 - 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. ER -