Machine Learning From Scratch
python implementations of various Machine Learning models and algorithms from scratch.
While some of the matrix operations that are implemented by hand (such as calculation of covariance matrix) are available from numpy I have decided to add these as well to make sure that I know how the linear algebra is applied.
The purpose of this project is purely self-educational.
Current implementations: Supervised Learning: Adaboost Decision Tree K Nearest Neighbors Linear Discriminant Analysis Linear Regression Logistic Regression Multi-class Linear Discriminant Analysis Multilayer Perceptron Naive Bayes Perceptron Random Forest Ridge Regression Support Vector Machine Unsupervised Learning: Gaussian Mixture Model Principal Component Analysis K-Means