1 Includes the latest research contributions on ML perspectives and applications.
2 Follows an algorithmic approach for data analysis in ML and includes real time engineering case studies.
3 Addresses the emerging issues in computing such as Deep Learning, Internet of Things and Data Analytics.
4 Focuses on ML techniques that are unsupervised and semi-supervised for unseen and seen data sets on commercial value-added research applications.
5 Discusses how the advancements on data science and computing domain open possibilities for cross disciplinary connections.