This project represents the assignment supplement of the machine learning Coursera course offered by Stanford University. Each week (ex) contains code written as starter code, which I completed to satisfy the assignment. All coding done by myself is contained within comments denoted as below:
% ====================== YOUR CODE HERE ========================== % ============================================================
Week by week summary:
- ex1: Linear Regression / Gradient Descent
- ex2: Logistic Regression
- ex3: Vectorization, intro to Neural Networks
- ex4: Feedforward Neural Networks / Backpropagation
- ex5: Regularization
- ex6: Support Vector Machines
- ex7: Principal Component Analysis, K-means algorithm
- ex8: Anomaly Detection, Recommender Systems