Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Moa | 559 | 78 | 11 | 6 months ago | 18 | April 03, 2023 | 38 | gpl-3.0 | Java | |
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. | ||||||||||
Awesome Python Machine Learning Resources | 77 | 5 months ago | 84 | |||||||
a collection of awesome machine learning and deep learning Python libraries&tools. 热门实用机器学习和深入学习Python库和工具的集合 | ||||||||||
Awesome_machine_learning_solutions | 58 | 6 years ago | ||||||||
A curated list of repositories for my book Machine Learning Solutions. | ||||||||||
Bikely | 21 | 8 months ago | 4 | Python | ||||||
Buy a second-hand bike at the best price | ||||||||||
Online Course Recommendation System | 20 | 5 years ago | n,ull | unlicense | Python | |||||
Built on data from Pluralsight's course API fetched results. Works with model trained with K-means unsupervised clustering algorithm. | ||||||||||
Tf Rec | 18 | a year ago | mit | Python | ||||||
Tf-Rec is a python💻 package for building⚒ Recommender Systems. It is built on top of Keras and Tensorflow 2 to utilize GPU Acceleration during training. | ||||||||||
Recommendation Engine | 8 | 6 years ago | Jupyter Notebook | |||||||
Recommendation engine and it's algorithms in python , R . | ||||||||||
Movies Recommender | 8 | 6 years ago | 4 | Matlab | ||||||
A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm. | ||||||||||
Movie_recommendation_system | 7 | 4 years ago | Jupyter Notebook | |||||||
A Movie Recommendation System based on the concept of content based filtering. | ||||||||||
Stanford Machine Learning | 5 | 4 months ago | mit | Jupyter Notebook | ||||||
My solutions to the assignments in the Machine Learning Specialization offered by Stanford University on Coursera. |