Machine Learning mini projects to implement Perceptron Learning, SVM - Primal and Dual, KNN, Decision Trees, Boosting with AdaBoost, AdaBoost with Co-ordinate descent, Bagging, PCA, Gaussian Naive Bayes classifier, Spectral Clustering, L1 and L2 Logistic Regression, and Gaussian Mixture Models using Expectation - Maximization (EM) algorithm from scratch in Python on UCI data sets such as Leaf Data Set, Sonar Data Set, SPECT Heart Data Set, Parkinsons Data Set and Mushroom Data Set.
Use any python interpreter to run the files. Various algorithms using the same datasets are grouped together in a specifc folder. The datasets are also available as .data files in the corresponding folder.