README.md

ML-Coursera (May 2, 2016 - July 24, 2016)

This repositiory contains coding assignmets from Machine Learning course offered by Stanford University. The course is offered on Coursera (https://www.coursera.org/learn/machine-learning/home/welcome) by Prof. Andrew Ng. This is best online course on Machine Learning I have came across. I would like to thank Prof. Andrew Ng for coming up with such nice and challenging coding assignments which provides insights into various Machine Learning Algorithms in great depth. All assginments are coded in MATLAB.

machine-learning-ex1

Linear regression to predict selling price of the house given features of the house

1. Linear regression with multiple variables
2. Feature Normalization
3. Normal Equations

machine-learning-ex2

1. Logistic regression model to predict whether a student gets admitted into a university.
2. Regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance.

machine-learning-ex3

1. One-vs-all logistic regression to recognize hand-written digits (Multi-class Classification).
2. Neural network to recognize hand-written digits (Feedforward Propagation).

machine-learning-ex4

1. Backpropagation algorithm for neural networks to recognize hand-written digit recognition. (Regularized Neural Networks)

machine-learning-ex5

1. Regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. (the effects of bias v.s. variance)

machine-learning-ex6

1. Support vector machines (SVMs) with Gaussian Kernels to build an Email spam classifier.

machine-learning-ex7

1. K-means clustering algorithm to compress an image.
2. Principal component analysis to find a low-dimensional representation of face images.

machine-learning-ex8

1. Anomaly detection algorithm to detect failing servers on a network.
2. Collaborative filtering to build a recommender system for movies.