This repository consists of my solutions to the weekly programming assignments of the Machine Learning MOOC which is offered by Stanford University at Coursera and is taught by Andrew Ng. As a master procrastinator, it took me more than 2 years to complete an 11-week course. On the bright side, the knowledge about Differential Calculus, Linear Algebra, Algorithms, Probability and Statistics that I could garner during my freshman and sophomore years really helped in understanding some of the contents of this course.

1. Linear Regression
2. Logistic Regression
3. Multi-Class Classification and Neural Networks
4. Neural Networks Learning
5. Regularized Linear Regression and Bias/Variance
6. Support Vector Machines
7. K-Means Clustering and Principal Component Analysis
8. Anomaly Detection and Recommender Systems

Although I think my grasp of the concepts are still pretty superficial, I'll try to update this markdown file to explain my solutions to the weekly programming exercises. I'll also try to upload my Quiz Feedbacks. 😄