mahine-learning

Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

Official PyTorch implementation of PSE/PSRN: Fast and efficient symbolic expression discovery through parallelized symbolic enumeration. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Gesture recognition library for Python

SoK: All You Need to Know About On-Device ML Model Extraction - The Gap Between Research and Practice

Joint Deep Neural Network for Simultaneous Object and Depth Detection

A repository featurin BeautifulSoup for effective web scraping, enabling data extraction from diverse websites with practical examples and guides.

Implements and benchmarks optimal demonstration selection strategies for In-Context Learning (ICL) using LLMs. Covers IDS, RDES, Influence-based Selection, Se², and TopK+ConE across reasoning and classification tasks, analyzing the impact of example relevance, diversity, and ordering on model performance across multiple architectures.

Text Classification using Machine Learning

Python framework for high quality confidence estimation of deep neural networks, providing methods such as confidence calibration and ordinal ranking

<머신 러닝 Q & AI>의 코드 저장소

EnviroMind is an AI-powered sustainability platform that unites healthcare, agriculture, recycling, climate tracking, and education into one web application. Through five intelligent tools — Dr R, AgroVision, EcoScan, PolluMap, and Madam A — it empowers individuals and communities to make informed, sustainable decisions aligned with the

A portfolio documenting my AIML internship at Elevate Labs, covering data preprocessing, EDA, regression, classification, and ensemble methods—plus capstone projects in fraud detection and real-time sign language recognition—showcasing practical skills, technical growth, and a strong commitment to AI/ML best practices..

Content-based Movie Recommendation System built using Python, Scikit-learn, NLP, and Streamlit.

This repository contains an email spam detection system built using logistic regression, achieving an accuracy of 98%. The model was trained on a comprehensive dataset of labeled emails to effectively classify spam and non-spam messages.

I'm a self-taught AI and Machine Learning developer, passionate about AI, Machine Learning, Computer Vision and learning new things. I have good experience working with the Python programming language and its libraries, and I am interested in computer vision and image processing using machine learning and deep learning algorithms.

Compressive Strength of Concrete determines the quality of Concrete. analyze the Concrete Compressive Strength dataset and build a Machine Learning model to predict the quality.

Resolución del desafío de la clase cuatro, última clase de la serie Inmersión de Datos de AluraLatam

Interactive web-based Tic-Tac-Toe game featuring Minimax and Q-Learning AIs, complete with training mode, hyperparameter tuning, and a modern responsive UI.

This machine learning project predicts house prices based on diverse features, utilizing a dataset containing historical housing data. With organized directories for data, source code, and models, it provides a foundation for accurate predictions and future enhancements. 🏡📈