Big data revolution is changing the lifestyle in terms of working and thinking environments through facilitating
improvement in vision finding and decision-making. But, big data science's technical dilemma is that there is no
knowledge that can administer and analyze large amounts of actively increasing data and pull out valuable
information. As data around the world grows rapidly and its distribution with real-time processing continues,
traditional tools for automated machine learning have become inadequate. However, conventional machine
learning (ML) approaches have been extended to meet the needs of other applications, but with increased
information or large data knowledge bases, there are significant challenges for ML algorithms for big data
analysis. This paper aims to facilitate understanding the importance of ML in the analysis of large data. It
contributes to understanding the implications and challenges in big data computational complexity, classification
imperfection and data heterogeneity. It discusses the capability to mine value from large-scale data for decision-
making and predictive analysis through data transformation and knowledge extraction. It will suggest the impact
of big data on real-time data analysis and discuss the extent to which machine learning can be used to analyze
large data through machine learning in big data analysis. It will also suggest the meaning and opportunity from
the point of view of encouraging feature research development in the field of ML using big data.
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