ML Mini Projects

These projects were all written for an ML course, in order to gain a better grasp of the fundamentals of machine learning. Projects include:

1. Building a hard-coded perceptron from scratch using MXNet NDArrays
2. Building a hard-coded Naive Bayes Classifier from scratch using only pandas and native python structures
3. Building a set of Convolutional Neural nets (CNNs) using MXNet. One piece of this project was to build and and then tune the parameters of a CNN in order to classify images of clothing items from the FashionMNIST. The second piece of the project was to build a more sophisticated CNN, by applying transfer learning, to classify between images of Cats and Dogs.
4. Building a series of Reinforcement Learning algorithms using native python