Supervised machine learning – image classification

In this section, we will discuss the image classification problem. The input dataset that we will use is MNIST (http://yann.lecun.com/exdb/mnist/), which is a classic dataset in machine learning, consisting of 28 x 28 grayscale images of handwritten digits.

 

 

The original training dataset contains 60,000 examples (the handwritten digit images along with the labels to train machine learning models), and the test dataset contains 10,000 examples (the handwritten digit images along with the labels as ground-truths, to test the accuracy of the models learned). Given a set of handwritten digits and images along with their labels (0-9), the goal will be to learn a machine learning model that can automatically identify the digit from an unseen image and assign a label (0-9) to the image:

1. A few supervised machine learning (multi-class classification) models (classifiers) will be trained using the training dataset
2. Then, they will be used to predict...