🔍 Optimize fake news detection using binary classification with a focus on convergence speed and efficiency over various numerical optimization methods.

This repository contains implementations of linear classification models developed from scratch using PyTorch, as part of Assignment 2. The assignment focuses on understanding discriminative linear classifiers, loss functions, optimization using gradient descent, and performance evaluation for both binary and multiclass problems.

This project demonstrates the implementation of a Softmax classifier from scratch, featuring both naive (loop-based) and vectorized versions for educational and performance comparison purposes. The implementation is based on CIFAR 10 dataset.

Step-by-step implementation of basic machine learning algorithms with performance comparisons against the sklearn implementation and solving classification and prediction problems.

This project detects digits using CNNs and OpenCV. It includes image preprocessing, model training on MNIST, real-time detection, and evaluation with accuracy metrics. Built with Python, TensorFlow/Keras, and Flask for deployment. Clone, install dependencies, and start detecting digits! 🚀

Lung Cancer Detection Using Machine Learning Methods

A software package for large-scale linear multilabel classification.

Scalable sparse linear models in Python

This Repository Contains all the Information and the Projects that I did at SAIL during my Internship

ML models built from scratch in Python 3.9.13

machine learning using python language to implement different algorithms

PyTorch implementation of 'CLIP' (Radford et al., 2021) from scratch and training it on Flickr8k + Flickr30k

Different machine learning approaches on classifying customers who are most likely to purchase an offer. Made with Jupyter Notebook, scikit-learn, and other helpful python packages.

This repository contains a PyTorch implementation for classifying the Oxford IIIT Pet Dataset using KNN and ResNet. The goal is to differentiate the results obtained using these two approaches.

Machine Learning Algortihms from scratch.

Linear classification. Logistic regression. Support vector machine.

Bengaluru House Price Prediction using Python (Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn). Machine learning predicts prices based on features like location, size, and bathrooms. Data preprocessing, Ridge Regression model, and evaluation metrics ensure accurate predictions. Clone, install, and run the script for precise Bengaluru house prices

Nicole Cruz Portfolio

Official implementation of Highly Scalable and Provably Accurate Classification in Poincaré Balls (ICDM regular paper 2021)

A Flash based backend api which runs a liner classifier model to predict closing price.