Nicole Cruz Portfolio

Scalable sparse linear models in Python

machine learning using python language to implement different algorithms

Laboratory works on Methods of Artificial Intelligence course

This repository is my learning and practice of of essential concepts and framework-techniques for Data Science and Machine Learning.

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 Algortihms from scratch.

Implementation of a Simple Perceptron (Simplest Neural network by Frank Rosenblatt) in C based on the example given example in the Veritasium video.

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.

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.

A Chinese guide book for learning Tensorflow from a starter.

Analyzed the effectiveness of COVID vaccine on 8 different age groups and trained 5 classification machine learning models to check whether the the vaccine developed immunity in 100k people from an age group or not.

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

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

Here you can find my practical works from ML course at MSU

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

A software package for large-scale linear multilabel classification.

Support Vector Machines (SVMs) from scratch, without dedicated packages, for the classification of linear and non-linear data.