Machine Learning Techniques and Tools: Merits and Demerits

Electronic data are generated in huge amounts in each and every field nowadays. These data contain useful information to predict the future from the current as well as the past data. Due to its huge size and different formats such as structured, unstructured, and semi-structured, manual processing of such data and prediction is a complex task for humans. To overcome 24this problem, the machines need to be trained to process and predict the future with the help of training and test data. To train the machine, various kinds of machine learning (ML) algorithms and tools are available under the category of classification, clustering, and prediction. It is essential to choose the significant algorithm and tool for the specific kind of data size, type, and its application domain. This chapter describes the various ML algorithms and tools in brief with the focus of its objective, working procedure, advantages, limitations, implementation tools, and the applicability of these algorithms in the real-time application domain.