Implementation of several machine learning models from scratch using Numpy for the course Machine Learning and Artificial Intelligence for Engineers at Carnegie Mellon University. A slight overview of the models covered

Homework 1
q1. Linear Regression
q2. EXperiments using gradient descent and mini batch gradient descent

Homework 2
q2_1 and q2_2. Logistic Regression using a line and a circular shape as classifiers
q3. Naive Bayes classifier approach for sentiment analysis on the ImDb dataset

Homework 3
q1. Kmeans, K Nearest Neighbors, Random Forest Regression and Logistic Regression on the same dataset with quantitative comparisons
q2. Gaussian Mixture Model: Expectation Maximization method for probabilistic assignment
q3. Principal Component Analysis

Homework 4
q2. Simple neural network for MNIST dataset
q3. CNN for image classification using MNIST dataset

Homework 5
q1. Policy Iteration
q2. Value Iteration
q3. Vanilla GAN