# Online machine learning course for R users

A while ago I took an online machine learning course offered by Andrew NG on Coursera platform. The course was spectacular although all the assignments MUST be submitted via MATLAB/OCTAVE. This would be a tedious effort for users who have spent a lot of time learning the basics of R language. Thus, I developed all the starter codes for R users in order to be able to carry out their assignments in R and submit them to Coursera directly from R. For more information visit the course Repo on Github.

## Screen-shots

A few screen-shots of the plots produced in R:

## Topics covered in the course and assignments

- Linear regression, cost function and normalization
- Gradient descent and advanced optimization
- Multiple linear regression and normal equation
- Logistic regression, decision boundary and multi-class classification
- Over-fitting and Regularization
- Neural Network non-linear classification
- Model validation, diagnosis and learning curves
- System design, prioritizing and error analysis
- Support vector machine (SVM), large margin classification and SVM kernels (linear and Gaussian)
- K-Means clustering
- Principal component analysis (PCA)
- Anomaly detection, supervised learning
- Recommender systems, Collaborative filtering
- Large scale machine learning, stochastic and mini-batch gradient descent, online learning, map reduce