Recommendation system is one of the most popular applications of Artificial Intelligence which attracts many researchers all over the globe. The advent of the Internet era has brought wide implementation of recommendation system in our everyday lives. There are many machine learning techniques which can be used to realize the recommendation system. Among all these techniques we are dealing with Content Based Filtering, Collaborative Based Filtering, Hybrid Content-Collaborative Based Filtering, k-mean clustering and Naive Bayes classifier. We have exploited these algorithms to their extreme in order to achieve the best possible precision and have presented a comprehensive comparative analysis. The strength of all these algorithms can be clearly realized by the significant enhancement in the accuracy, depicted by the experimental analysis taking cold start problem into consideration.

Machine Learning is playing a predominant role across various domains. However traditional Machine Learning algorithms are becoming unsuitable for majority of applications as the data is acquiring new characteristics. Sensors, devices, servers, Internet, Social Networking, Smart phones and Internet of Things are contributing the major sources of data. Hence there is a paradigm shift in the Machine learning with the advent of Big Data. Research works are in evolution to deal with Big Data Batch and stream real time data. In this paper, we highlighted several research works that contributed towards Big Data Machine Learning.