Multiclass-Semantic-Segmentation-with-U-NET

Multiclass Semantic Segmentation using U-NET architecture on aerial drone imagery

Semantic-Segmentation-with-UNET

In one line, it is pixel-wise classification. The deep learning model we use here is UNET model designed for Biomedical Image Segmentation originally proposed in the paper entitled: U-Net: Convolutional Networks for Biomedical Image Segmentation by Olaf Ronneberger, Philipp Fischer, Thomas Brox.

google-playstore-analysis

A project on comprehensive analysis of the Google Playstore Apps dataset.

CardiacMRI_3D_UNet_Pytorch

PyTorch code for 3D-UNet for Cardiac MRI scan

Predicting-Presence-of-Heart-Disease-using-Machine-Learning-Techniques

On the dataset of 13 features and 1 target variable that indicates if the heart disease is present or not, we use classification algorithms: Linear Regression, Naive Bayes Classifier, KNN Classifier in this notebook and compare them in terms of accuracy.