Machine learning is a field which gives computers the ability to learn and make predictions on data. This STTP is aimed at giving hands-on training to the Faculty Members and Research Scholars on various aspects of Machine learning and Data Analytics techniques. In recent years, there has been a rapid development on applications of machine learning which has a significant impact on education, research and lifestyle.  Machine learning is about designing programs that can learn without being explicitly programmed. Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Most industries have recognized the value of machine learning technology. By gleaning insights from this data, often in real time, organizations are able to work more efficiently or gain an advantage over competitors. Some of the sectors which exhaustively use machine learning and Data Analytics are Financial services, Governments, Health care, Transportation, Oil and gas, Hospitality, to name a few. Due to new computing technologies, many advancements has taken place in the machine learning area. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. To support the proliferation of automation and smart devices that are using machine learning and Data Analytics, it is necessary to improve the state-of-the-art technologies in these areas to achieve better performance in terms of clustering, classification, prediction, direct and indirect modellings.