Developed deep learning models for image classification on MNIST, CIFAR-10, and Cats vs Dogs datasets. Used normalization, data augmentation, and transfer learning with MobileNetV2. Achieved ~98% (MNIST) and ~90% (CIFAR-10). Evaluated using accuracy, confusion matrix, ROC curve, and tested on custom images.