The MNIST dataset is a popular image classification dataset, consisting of 60,000 training images and 10,000 testing images of handwritten digits. In this Python project, we will use neural networks to classify the images into their respective digits. We will preprocess the data, build a neural network model using TensorFlow, and train the model using the training dataset. We will evaluate the model's accuracy on the testing dataset and make predictions on new images. This project is a great way to gain hands-on experience with neural networks and image classification, and is a common starting point for beginners in the field of machine learning.