My projects from the Stanford Machine Learning course offered on Coursera by Professor Andrew Ng.

Probabilistic Matrix Factorization with Social Trust for Recommendation (Ma et al. SIGIR 2009)

Recommend movies to user based on the ratings provided.

A reccommender system for jobs using Deep Auto Encoders and Fuzzy Clustering

works for ML course by Andrew Ng

Machine Learning Algorithms for the programming tasks of Stanford online course from Andrew Ng on Coursera

This repository shows code of programming tasks which I completed during Machine Learning course on Coursera.

Nonnegative matrix factorization with DAG constraints. A probabilistic formulation, variational learning.

All the graded assignments of the course

Machine Learning (Stanford University) Week 9 assignments solutions

Recommendation Algorithm with Collaborative Filtering Technique

Solutions to all the homework assignments with my experiments as well

Movie Recommendation using Cascading Bandits namely CascadeLinTS and CascadeLinUCB

A Probabilistic Graphical approach to detect different types of shilling attacks on Recommender Systems.

Exercises I solved for the Machine Learning course at Coursera by Andrew Ng.

This is my BTech minor research project that was published in IEEE Conference. Here I implemented various Machine Learning Algorithms specially recommender systems to predict diseases based on individuals medical history and current symptoms. The model can also predict various other disease one can predict in future at a given age.

Machine Learning Course taught by Andrew Ng.

Andrew Ng's Machine Learning Course

Movie recommendation system based on collaborative filtering trained ratings

Recommendation system for the jester (joke) dataset using collaborative filtering and K-means clustering algorithms.