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Machine learning involves detecting patterns in data and using them to predict future outcomes.
Choice of the Machine learning model depends on whether we want to predict or infer.
The learning method can be parametric or non-parametric, and supervised or unsupervised.
Usually, 70-80% of the available data is used for training the algorithm and 20-30% for testing.
Data is classified using Maximum Margin Classifiers for separable data, Support Vector Classifiers for non-separable data, and Support Vector Machines for Non Linear class boundaries.
Support vector classifier is a soft margin classifier.
Support vector machine (SVM) is an extension of the support vector classifier which uses Kernels to create non linear boundaries.