This repository contains Python implementation of ML algorithms, built either from scratch or by using sk-learn library.

• Feature Engineering
• Linear Regression with L2 regularization
• Linear Regression with SGD (Stochastic Gradient Descent)
• Kernel Linear Regression
• Logistic Regression with SGD
• Grid Search
• Gradient Boosting Trees
• Decision Tree
• Random Forest
• Bootstrap Resampling
• Naive Bayes
• Support Vector Machines (SVM)
• Sequential minimal optimization (SMO)
• Gaussian Mixture
• K-means
• KNN (K Nearest Neighbors)
• Principal component analysis (PCA)
• Softmax Classification with SGD
• Multi-layer perceptron (MLP) classification with SGD
• Perceptron