Machine learning techniques for cognitive decision making

Machine learning algorithms in cognitive computing for decision making can help out how to achieve significant solutions by generalizing a learned model from environmental pattern instances. This technique is frequently practicable and economical where manual rigid rule based abstract programming is not suitable. As more training input patterns are obtainable, better-determined tasks can be attempted. As a result, machine learning is extensively used in cognitive computing and artificial intelligence for handling structured, unstructured and multimedia big data. However, evolving fruitful machine learning cognitive applications involves a considerable extent of concept that is not available in general theories. This paper will analyze primary modules of machine learning approaches and attempt to present friendly real world example of cognitive teacher appraisal. The first section will sightsee the meaning of machine learning, deliberate the cognitive abilities it can generate. The second and third section discusses the practical procedure and issues for solving cognitive problems. The next Five section will define concept of machine learning methods and its application problem domain. The last section shows comparison of machine learning algorithm capability and limitations.