Implementations of various machine learning methods
Logistic Regression using iteratively-reweighted least squares, conjugate gradients, Broyden-Fletcher-Goldfarb-Shanno (bfgs), and batch stochastic gradient descent
Rosenblatt's Perceptron
Both Quadratic and Linear Discriminant Analysis
Multinomial and Bernoulli Naive Bayes
Kernelized and standard k nearest neighbors algorithm
Classification and Regression Trees
Random Forest
General: numpy, scipy, and scikit-learn
Specific: Linear Models have the option of showing learning rates, so matplotlib and seaborn are needed
Can install by using the command $ pip install LibML