Implementation of Machine Learning Algorithms

This started as part of a project for my graduate course - Machine Learning (CS536) and I extended it further for my own learning. In order to understand the workings of the different machine learning algorithms, I started implementing them using just Numpy and Python without the aid of any external libraries. Yes! - Bye bye sklearn, tensorflow, pytorch and keras! A list of the algorithms I have implemented till now with links to their code on Github:

• Supervised Learning

  • Regression Algorithms

    • Linear Regression

    • Ridge Regression

    • Lasso Regression

    • Decision Tree Regressor

    • K-Nearest Neighbors Regressor

    • Neural Network Regressor

  • Classification Algrithms

    • Binary Logistic Regression

    • Softmax Regression

    • Decision Tree Classifier

    • Adaboost Binary Classifier

    • K-Nearest Neighbors Classifier

    • Neural Network Classifier

• Unsupervised Learning

  • K-Means Clustering

  • Gaussian Mixture Models

  • Variational Auto-Encoders (In Progress)