ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Website: www.ijircce.com Vol. 5, I ssue 2, February 2017 Copyright to IJIRCCE DOI: 10.15680/IJIRCCE.2017. 0502001 1301 A Survey on Machine Learning: Concept, Algorithms and Applications Kajaree Das 1 , Rabi Narayan Behera 2 B.Tech, Dept. of I.T., Institute of Engineering and Management, Kolkata, India 1 Asst. Professor, Dept. of I.T., Institute of Engineering and Management, Kolkata, India 2 ABSTRACT: Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational approaches, to an independent research discipline that has not only provided the necessary base for statistical-computational principles of learning procedures, but also has developedvarious algorithms that are regularly used for text interpretation, pattern recognition, and a many other commercial purposes and has led to a separate research interest in data mining to identify hidden regularities or irregularities in social data that growing by second. This paper focuses on explaining the concept and evolution of Machine Learning, some of the popular Machine Learning algorithms and try to compare three most popular algorithms based on some basic notions. Sentiment140 dataset was used and performance of each algorithm in terms of training time, prediction time and accuracy of prediction have been documented and compared. KEYWORDS: Machine Learning, Algorithm, Data, Training, accuracy I. INTRODUCTION Machine learning is a paradigm that may refer to learning from past experience (which in this case is previous data) to improve future performance. The sole focus of this field is automatic learning methods. Learning refers to modification or improvement of algorithm based on past “experiences” automatically without any external assistance from human. While designing a machine (a software system), the programmer always has a specific purpose in mind. For instance, consider J. K. Rowling’s Harry Potter Series and Robert Galbraith’s Cormoran Strike Series. To confirm the claim that it was indeed Rowling who had written those books under the name Galbraith, two experts were engaged by The London Sunday Times and using Forensic Machine Learning they were able to prove that the claim was true. They develop a machine learning algorithm and “trained” it with Rowling’s as well as other writers writing examples to seek and learn the underlying patterns and then “test” the books by Galbraith. The algorithm concluded that Rowling’s and Galbraith’s writing matched the most in several aspects. So instead of designing an algorithm to address the problem directly, using Machine Learning, a researcher seek an approach through which the machine, i.e., the algorithm will come up with its own solution based on the example or training data set provided to it initially. A. MACHINE LEARNING : INTERSECTION OF STATISTICS AND COMPUTER SCIENCE Machine Learning was the phenomenal outcomewhen Computer Science and Statistics joined forces. Computer Science focuses on building machines that solve particular problems, and tries to identify if problems are solvable at all. The main approach that Statistics fundamentally employs is data inference, modelling hypothesises and measuring reliability of the conclusions. The defining ideaof Machine Learning is a little different but partially dependent on both nonetheless. Whereas Computer Science concentrate on manually programming computers, MLaddressesthe problem of getting computers to re-program themselves whenever exposed to new data based on some initial learning strategies provided. On the other hand, Statistics focuses on data inference and probability, Machine Learning includes additional concerns about the