harshvardhanmunda Goto Github PK

breast-cancer-classification

We’ll build a classifier to train on 80% of a breast cancer histology image dataset. Of this, we’ll keep 10% of the data for validation. CNN

breast-cancer-classification-cnn

We’ll build a classifier to train on 80% of a breast cancer histology image dataset. Of this, we’ll keep 10% of the data for validation. CNN (Convolutional Neural Network)

coursera-dsa

Coursera's Data Structures and Algorithms Specialization

covidaid

COVID-19 Detection Using Chest X-Ray

data-structures-and-algorithms-specialization-university-of-california-san-diego

detecting-covid-19-with-chest-x-ray-using-pytorch

Learned to use PyTorch to create and train a ResNet-18 model and apply it to a Chest X-Ray Radiography Dataset.

fifa-20-data-exploration-

Learn to use data Exploration techniques in order to uncover some initial patterns, insights and interesting points in your dataset. We are going to use a dataset consisting 5 CSV files, consisting of the data related to players in FIFA video game. We will clean and prepare it by dropping useless columns, calculating new features for our dataset and filling up the null values properly. and then we will start our exploration and we'll do some visualizations.

house-prices-advanced-regression-technique-

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting.

house-prices-predictor

prediction-of-house-project

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting

test