ML-Projects Project 0: Face Recognition and Classification Project 1: Covid-19 Data Visualization Prediction of Confirmed cases of various countries (just basic implementation) Project 2: Handwritten digit recognition trained MNIST dataset on Neural Network with one hidden layer of 128 neurons using Loss Function: sparse categorical crossentropy, optimizer: Adam attaining an accuracy of ~98% Project 3: Titanic Survival Classification develop the neural network from scratch Best accuracy of 80% Project 4: Transfer Learning using MobileNet Image CLassification of Flowers used concept of Transfer Learning can attain accuracy of ~99% Project 5: Image Classification using CNNs worked on fashion_mnist dataset implemented CNNs can attain accuracy of ~92.8% also tried to implement on CIFAR-10 dataset Project 6: Face Verification (Siamese network) can be deployed as application for fae verification used Siamese network Project 7: Sentiment Classification) used imdb reviews dataset implemented RNNs from scratch (vocabulary intialization, embedding, RNN architecture, training & updating parameters, Prediction)