Fun Q: A Functional Introduction to Machine Learning in Q clone this project and start q with any of the following: q fun.q q plot.q q linreg.q q onevsall.q q nn.q q kmeans.q q knn.q q em.q q recommend.q q decisiontree.q q adaboost.q q randomforest.q q supportvectormachine.q q hiragana.q you can then read the comments and run the examples one by one. topics include: Plotting Least Squares Regression Gradient Descent Logistic Regression Binary Classification Evaluation Metrics One vs All Logistic Regression Neural Networks K-Means/Medians Clustering Hierarchical Clustering Analysis (HCA) Expectation Maximization (EM) K-Nearest Neighbors (kNN) Markov Clustering Algorithm (MCL) Naive Bayes Decision Tree (ID3,C4.5) Adaboost Random Forest Google PageRank Content-Based Filtering (Recommender Systems) Collaborative Filtering (Recommender Systems) Vector Space Model (tf-idf) Support Vector Machine (SVM)