Description
This project is an exploratory exercise in machine learning using the classic MNIST handwritten digit dataset. The MNIST dataset is commonly used in early machine learning exercises because it is small, accessible, and there is relatively low friction for building a well-performing classifier. The task here is to build a machine learning model to detect handwritten digits and properly classify them. Accurately classifying handwritten digits is a key component of optical character recognition (OCR) software, so this project provides key insight into some low hanging fruit in applied machine learning.