In this era, Machine Learning (ML) is persistently releasing its power in extensive variety of applications. It has been observed in previous years partly owing from advert of massive data. Huge information empowers machine learning calculations to reveal all the designs and make more precise predictions than ever before. In another way, machine learning presents challenges in field of data mining and big data. In this paper, we discussed what machine learning is and how it is related with big data. Here, we have introduced some phases of ML and the tools used to perform accurate prediction and how it is helpful in future tasks. This paper also has been discussed the opportunities and challenges associated with ML.

Machine Learning, Big Data, Data Mining, Knowledge Discovery

[1] M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, andprospects," Science, vol. 349, pp. 255-260, 2015.
[2] J Gantz, D Reinsel, The digital universe decade—are you ready (EMC,Hopkinton, 2010)
[3] J Manyika, M Chui, B Brown, J Bughin, R Dobbs, C Roxburgh, AH Byers, Bigdata: the next frontier for innovation, competition, and productivity (McKinseyGlobal Institute, USA, 2011)
[4] Q Wu, G Ding, Y Xu, S Feng, Z Du, J Wang, K Long, Cognitive internet ofthings: a new paradigm beyond connection. IEEE Internet Things J1(2), 129–143 (2014)
[5] N. Japkowicz and M. Shah, Evaluating Learning Algorithms: A Classification Perspective:Cambridge University Press, 2011.
[6] Jyoti Arora, Ambica Sood, "A Survey on knowledge extraction Approaches from Big Data and Rectifying Misclassification strategies", International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.187-200, 2017.
[7] O. Okun, G. Valentini, (Eds.), Supervised and Unsupervised Ensemble Methods and their Applications Studies in Computational Intelligence, vol. 126, Springer, Heidelberg, 2008.
[8] ] Nelles, Oliver. "Unsupervised Learning Techniques." In Nonlinear System Identification, pp. 137-155. Springer Berlin Heidelberg, 2001.
[9] Burch, Carl. "A survey of machine learning." Tech. report, Pennsylvania Governor`s School for the Sciences (2001).
[10] Jones, Nicola. "The learning machines." Nature 505, no. 7482 (2014): 146.
[11] Mitchell, Tom M. "Machine learning. WCB." (1997).
[12] Zheng, Jun, Furao Shen, Hongjun Fan, and Jinxi Zhao. "Anonline incremental learning support vector machine for large-scale data." Neural Computing and Applications 22,no. 5 (2013): 1023-1035.
[13] Cortes, Corinna, and Vladimir Vapnik. "Support-vectornetworks." Machine learning 20, no. 3 (1995): 273-297.
[14] Burch, Carl. "A survey of machine learning." Tech. report,Pennsylvania Governor`s School for the Sciences (2001).
[15] Dong, Xu, Ying Li, Chun Wu, and Yueming Cai. "Alearner based on neural network for cognitive radio."In Communication Technology (ICCT), 2010 12th IEEEInternational Conference on, pp. 893-896. IEEE, 2010.
[16] Safatly, Lise, Mario Bkassiny, Mohammed Al-Husseini,and Ali El-Hajj. "Cognitive radio transceivers: RF, spectrum sensing, and learning algorithmsreview." International Journal of Antennas andPropagation 2014 (2014).
[17] Weber, Markus, Max Welling, and Pietro Perona. "Unsupervised learning of models for recognition." In European conference on computer vision, pp. 18-32. Springer, Berlin, Heidelberg, 2000.
[18] Galindo-Serrano, Ana, and Lorenza Giupponi. "Distributed Q-learning for aggregated interference control in cognitive radio networks." IEEE Transactions on Vehicular Technology 59, no. 4 (2010): 1823-1834.
[19] Sutton, Richard S. "Learning to predict by the methods of temporal differences." Machine learning 3, no. 1 (1988): 9-44.
[20] D Yu, L Deng, Deep learning and its applications to signal and information processing. IEEE Signal Proc Mag 28(1), 145–154 (2011)
[21] Y Bengio, Learning deep architectures for AI. Foundations Trends Mach Learn 2(1), 1–127 (2009).
[22] D Peteiro-Barral, B Guijarro-Berdiñas, A survey of methods for distributed machine learning. Progress in Artificial Intelligence 2(1), 1–11 (2012)
[23] H Zheng, SR Kulkarni, HV Poor, Attribute-distributed learning: models, limits, and algorithms. IEEE Trans Signal Process 59(1), 386–398 (2011)
[24] Y.Fu, B Li, X Zhu, C Zhang, Active learning without knowing individual instance labels: a pairwise label homogeneity query approach. IEEE Trans Knowl Data Eng 26(4), 808–822 (2014)
[25] B Settles, Active learning literature survey (University of Wisconsin, Madison, 2010)