MATLAB assignments and scripts for Coursera's Machine Learning Course
Throughout the 11 weeks of this course, assignments were project based and covered: Linear regression, Logistic regression, Regularization, Neural Networks, Support Vector Machines, Unsupervised Learning, K-means Clustering, Principal Component Analysis, Anomaly Detection, Reccomender Systems, and Large Scale Machine Learning
These are the processes/scripts completed in each exercise:
Exercise 1
Computing Cost (for One Variable), Gradient Descent (for One Variable), Feature Normalization, Computing Cost (for Multiple Variables), Gradient Descent (for Multiple Variables), and Normal Equations
Exercise 2 in Week 3
Sigmoid Function, Logistic Regression Cost, Logistic Regression Gradient, Predict, Regularized Logistic Regression Cost, and Regularized Logistic Regression Gradient
Exercise 3 in Week 4
Regularized Logistic Regression, One-vs-All Classifier Training, One-vs-All Classifier Prediction, and Neural Network Prediction Function
Exercise 4 in Week 5
Feedforward and Cost Function, Regularized Cost Function, Sigmoid Gradient, Neural Network Gradient (Backpropagation), and Regularized Gradient
Exercise 5 in Week 6
Regularized Linear Regression Cost Function, Regularized Linear Regression Gradient, Learning Curve, Polynomial Feature Mapping, and Validation Curve
Exercise 6 in Week 7
Gaussian Kernel, Parameters (C, sigma) for Dataset 3, Email Preprocessing, and Email Feature Extraction
Exercise 7 in Week 8
Find Closest Centroids (k-Means), Compute Centroid Means (k-Means), PCA, Project Data (PCA), and Recover Data (PCA)
Exercise 8 in Week 9
Estimate Gaussian Parameters, Select Threshold, Collaborative Filtering Cost, Collaborative Filtering Gradient, Regularized Cost, and Regularized Gradient