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There are 3 most important algorithms in machine learning domain. They are: 1. Supervised learning – in this type of algorithm, machine maps or assigns the given input to the output. And the output for a given input is known in advance. Here regression and classification problems are solved, labelled data is used for training and this algorithm is mainly used to predict modelling. The popular algorithms used here are linear regression, SVM, neural networks, and decision trees. 2. Semi-supervised learning – these lie between supervised and unsupervised learning. It is used in the real-world (to produce the desired results) where the data collected are a combination of labelled and unlabeled data. 3. Unsupervised learning – here the output for a given inputs is unknown. It is used in clustering problems and anomaly detection. Here unlabeled data is used and is used for descriptive modelling. The popular algorithms used here are association rule and k-means clustering.

Any other algorithms?

You have to learn logistics regression and least square regression. Logistic regression measures the relationship between the one or more independent variables and categorical dependent variable.

Suresh, logistic regression and least square regression are not separate algorithms. Instead they are the part of supervised learning algorithm.