MADS: Model Analysis & Decision Support

📖Notes and remarks on Machine Learning related papers

A Text / Speech Summarizer

Numerical Analysis Projects

A Cloud Based Personalised Recommendation System for movies and books.

Predicting Nobel Physics Prize winners. Final project for Harvard CS109a 2017 edition https://github.com/covuworie/a-2017.

Complete concepts behind implementing a Recommendation System using Association Rules, Collaborative Filtering, and Matrix Factorization.

Recommendation on data from the IBM Watson Studio platform

(Class) Computer Aided Analysis and Design (Optiomisation algorithms)

This is a movie recommendation system that recommends movie based on the ratings given by the user, uses user-user collaborative filter, item-item collaborative filter and matrix factorisation

Evaluating k-nearest neighbors and singular value decomposition techniques for collaborative filtering recommender systems

Movie recommendations

EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.

This website applies a recommendation system and continuous learning.

Project 3 in Data Scientist Nanodegree with Udacity. Build a recommender engine for IBM Watson.

This projects shows some techniques for recommendation engines using data from the IBM Watson Studio Platform.

Articles recommendation engine for IBM Watson Studio platform

Performed EDA, created user-article matrix, calculated similarity using dot product, implemented Rank-Based, User-User CF, Content-Based, and Matrix Factorization, evaluated model with precision, recall, and F1-score.

analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations on new articles they will like.

In the IBM Watson Studio, there is a large collaborative community ecosystem of articles, datasets, notebooks, and other A.I. and ML. assets. Users of the system interact with all of this. This is a recommendation system project to enhance the user experience and connect them with assets. This personalizes the experience for each user.