When machine learning algorithms are applied to huge amount of data, we found difficulties to process such huge data. Now new approaches are being adopted because existing machine learning libraries doesn't have enough resources to process large datasets. So new libraries (CUDA, MapReduce, and Dryad) are adding up for concepts like parallel computing. Here we will take account of GraphLab, Apache MahoutTM, and Jubatus to get the exposure of famous academics and industrial results. Looking at the traditional machine learning techniques, tasks like to handle the data which is distributed identically or in batch mode becomes impossible and there is requirement to develop new algorithms to overcome with the existing difficulties faced by these traditional ML algorithms. The objective of this chapter is to provide overall view of developed algorithms and paradigm shifts of current big data analysis using machine learning approach to compute data. Here we will explore that the machine learning field has great impact on cloud computing paradigm. In first step we deploy various tool to the cloud like libraries and statistics tools. In second step we embed plugins with current tools in order to make Hadoop cluster on the cloud so that working programs can run on it. In third step libraries of machine learning algorithms are deployed and used for data intensive computing.