Comparison Between Different Machine Learning Algorithms

Machine learning is a major application of AI it is a phenomena by which system automatically learn and improve from real world experience in form of data on which it is trained or by observing the surroundings. Machine learning is changing the planet by transforming all segments including healthcare services, education, transport, food, entertainment, and different production line and lots of more. Machine learning plays a great role in changing our lives as well as industries in sector like housing and applications, cars, shopping, food ordering, etc. One of the application of ML is character recognition. Character recognition is used to convert handwritten or printed documents or image characters of document to computer codes. The basic process involves examining the text of a document and translating the characters into code that can be used for data processing. Hardware, like an optical scanner or specialized circuit card is employed to repeat or read text while software typically handles the advanced processing. Software can also take advantage of machine learning algorithms to implement more advanced methods of intelligent character recognition, like identifying languages or styles of handwriting. In this era there are various machine Learning algorithms like a linear regression, Convolutional Neural Network(CNN), kNN, K-Means, Random Forest etc. In this paper we are going to compare the results between between linear regression and Convolution Neural Network(CNN) over a same data-set of 0-9 digits.