Fundamental Models in Machine Learning and Deep Learning
Tatwadarshi P. Nagarhalli, Ashwini M. Save, Narendra M. Shekokar · 2021
The resurgence of applied informatics (AI) has revolutionised the whole of the computing industry, which in turn has revolutionised almost all the possible sectors of industry. AI is the ability of the machines to think and learn in order to solve a problem by making smart decisions. Some of the fundamental and basic machine learning (ML) algorithms include linear regression, logistic regression, support vector machine, random forest, artificial neural networks, decision trees, k-means clustering and Apriori algorithm. Based on these two parameters, ML models can be classified as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Once the labels have been identified for the unlabelled data, any supervised learning models can be used for regression or classification. The hypothesis function for logistic regression is given as where Like linear regression, even logistic regression is non-tolerant with respect to the correlation among input features.