Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
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Updated
Oct 26, 2023 - Python
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.
A robust classifier for few-training-data problem based on a distributionally robust optimization framework
A tool for clustering images using deep learning features and visualizing the results in organized grids.
Audio Pattern Recognition project - Music Genres Classification
I am partaking in research with my professor Dr. Boxiang Dong at Montclair State University in using deep learning techniques for anomaly detection. This project is to help with that research, specifically in implementing Machine Learning classifiers and more.
Machine Learning tasks and mini projects based on my learning in a Datascience bootcamp in Udemy
Simpsons Members Recognizer Supervised Machine Learning Algorithm.
🔢 A self-introduction to machine learning. Simple application that recognises handwritten/mouse drawn digits from 0-9.
Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.
A k-Nearest Neighbors (k-NN) Classifier for the Iris Flower Dataset implemented in Python using NumPy and SciPy. This project calculates distances between new and training samples, finds the k nearest neighbors, and predicts the types of new samples. Accuracy is evaluated against known labels.
This program is a real-time face recognition system that uses OpenCV and k-Nearest Neighbors (k-NN) to detect and label faces from a webcam feed.
Breast Cancer Prediction using K-NN, Machine Learning/Artificial Intelligence Algorithm on Python sckilearn
Testing face recognition power of different algorithms on ORL database.
Benchmarks of collaborative filtering techniques and optimizing K-NN and SVD for recommendation accuracy
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