A Systematic Review of Techniques, Tools and Applications of Machine Learning

Machine Learning is a part of Artificial Intelligence. A branch of artificial Intelligence(AI), that offers the capability to the system by learning on their own and work better from experience without human intervention. By using machine learning (ML), software applications become more precise for outcome prediction with no requirement of explicit programmed. Machine learning has a fundamental concept i.e. building of algorithms for accepting input data and with the help of statistical analysis they can predict the output. Nowadays, machine learning has gained vast popularity, and its algorithms are used in every field like object detection, pattern recognition, text interpretation, and different research areas. One of the basic goals of machine learning is to teach computers to use the data to solve a particular problem. A fair range of machine-learning applications include e-mail classification training to distinguish spam from non-spam messages, fraud detection, etc. This paper aims at varied machine learning methods, algorithms, and tools required to run the machine learning projects. It also focuses on the advancements that are administered in order so that the current researchers can be benefited out of it.