• Paras Nath Mishra

Machine learning research is essential both for answering these basic scientific and technical issues and for the very practical computer software it has developed and deployed in a variety of applications. Machine Learning (ML) aims to answer the issue of how to create machines that learn on their own. It is at the confluence of computer science and statistics, as well as at the heart of artificial intelligence and data science, and is one of today's fastest developing technological areas. The development of novel learning algorithms and theory, as well as the continuing growth in the availability of online data and low-cost computing, have fuelled recent advances in machine learning. Data-intensive machine-learning techniques are increasingly being used in research, technology, and commerce, resulting in more evidence-based decision-making in a variety of fields such as medical care, industry, education, financial planning, police, and advertisement. In this paper we have discussed the current advances, different views and future opportunities of the ML.

Deep Learning, Learning Algorithms, Machine Learning, Supervised Learning, Un-Supervised Learning.

"Machine Learning: Advances, Views, and Opportunities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.39-45, March-2019, Available :http://www.jetir.org/papers/JETIRFT06010.pdf

"Machine Learning: Advances, Views, and Opportunities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp39-45, March-2019, Available at : http://www.jetir.org/papers/JETIRFT06010.pdf