Basic implementation of machine learning algorithms from scratch to understand them better.
- Simple Linear Regression
- Multiple Linear Regression (using matrix solution)
- Gradient Descent - batch and stochastic
- Logistic Regression
- Support Vector Machines
- Decision Trees
- K-Means Clustering
- Neural Network ( with fixed hidden layer + batch gradient descent)