Machine Learning in Practice

Machine learning algorithms are categorized into supervised, unsupervised and semi-supervised. This presentation will discuss how to analyze a given dataset and applying an appropriate model. The steps are: getting and cleaning data, extracting and selecting features and finally developing an appropriate classifier. Some of the popular classifiers such as Naïve Bayes, SVM and Neural Network will be discussed. Generalizing the algorithm on test dataset and calculating error rate is an important part in developing a robust model on any given dataset. The algorithms will be discussed briefly with some practical examples.