This repository contains MATLAB Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.
-
Programming Exercise 1: Linear Regression
In this exercise, you will implement linear regression and get to see how it work on real world datasets. -
Programming Exercise 2: Logistic Regression
In this exercise, you will implement logistic regression and apply it to two different datasets. -
Programming Exercise 3: Multi-class Classification and Neural Networks
In this exercise, you will implement one-vs-all logistic regression and feedforward propagation for neural networks to recognize handwritten digits. -
Programming Exercise 4: Neural Network Learning
In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition. -
Programming Exercise 5: Regularized Linear Regression and Bias vs Variance
In this exercise, you will implement regularized linear regression and polynomial regression and use it to study models with different bias-variance properties. -
Programming Exercise 6: Support Vector Machines
In this exercise, you will implement support vector machine (SVM) with Gaussian Kernels and you will be using support vector machines (SVMs) to build a spam classifier. -
Programming Exercise 7: K-means Clustering and Principal Component Analysis
In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. -
Programming Exercise 8: Anomaly Detection and Recommender Systems
In this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering to build a recommender system for movies.