I am using this repository to give an overview of basic machine learning techniques and showcase some of the things I have learned at university.
- Project polynomial_regression.py is a short demonstration of underfitting and overfitting using Polynomial Regression.
- Project bayesian_linear_regression.py calculates the Predictive Distribution using Bayesian Linear Regression and plots the result Variance to showcase certainty around existing data points and uncertainty elsewhere.
- Project support_vector_machines.py separates a data set using Support Vector Machines to obtain decision boundaries between the two resulting clusters.
- Project multiclass_logistic_regression.py uses the famous MNIST image data set to classify handwritten digits using Multiclass Logistic Regression and Stochastic Gradient Descent.
All projects should be easy to adapt, but at some points I assumed knowledge of the underlying formulas. Please feel free to reach out if you would like to discuss or collaborate further on it!