This repository will consist homework of assignments and projects from the machine learning class at the University of North Carolina at Charlotte.

Linear Regression and Gradient Descent

Regularization: Penalizing larger sample values that may result in overfitting our training model.

Logistic Regression | PCA | Bayes Naive Classification | K-Principle Components

Support Vector Machine (SVM) | Support Vector Regression (SVR)

Pytorch | Optimizers: Adam and Schotastic Gradient Descent (SGD)

Fully Connected Neural Networks (FCNN)

Convolutional Neural Networks (CNN)| Regularization | Residual Networks(ResNet)

Predicting The Future Returns of the S&P 500

machine learning models: SVM Fully Connected Neural Network Convolutional Neural Network