Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
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Updated
Oct 13, 2023 - q
Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
A news application in Android that offers the recommended, summarized news articles in as short and crisp format from multiple news sources per user’s choice. Key Features: Recommendation System Engine (gives recommended news articles), NLP Summarization (gives a 5 sentence summarized for each articles), Popularity-based Trending System (to stay…
Projects for Udacity Data Scientist Nanodegree
Clustered Compositional Embeddings
A recommendation system for products using PySpark
Code for the project: "Analysis of Recommendation-systems based on User Preferences".
This is an implementation where content based and collaborative filtering recommendation methods are used for recommending courses.
This repository has the solutions to multiple assignments done during the course on topics like supervised leering, deep learning
Reccomender Sysstems with Sckit Surpirse
The Book Recommender System is a collaborative filtering-based approach that suggests personalized book recommendations based on user preferences and similarities. The system provides a user-friendly interface through Streamlit for an enhanced user experience.
An app to search duos on your games!
Machine learning concepts implemented in python.
A platform to connect music lovers across the globe!!
Scrumptious Suggestions is a data powered website that suggests it's users the best recipes based on their input ingredients.
Implement the Next Item Recommendation with Self-Attention (AttRec) model to the Recommenders 1.2.0 like format
A Non Negative Matrix Factorization Recommendation System that suggests content for a social network application
A smart recipe recommendation system that suggests recipes based on ingredient similarities. This project is done in PySpark
This is a content-based movie recommender system that utilizes machine learning and cosine similarity to suggest movies to users based on their preferences. The system uses the tmdb dataset and provides a user-friendly interface through Streamlit for an enhanced user experience.
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