jns4u/ML-From-Scratch

Bare bones Python implementations of various Machine Learning models and algorithms.

Machine Learning From Scratch

Python implementations of various Machine Learning models and algorithms from scratch.

Some matrix and vector operations such as calculations of covariance and correlation matricies are implemented by hand even though there are numpy methods that enable these computations.

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