The article explores the process of image classification using the MNIST dataset, which consists of grayscale images of digits from 0 to 9, by implementing logistic regression. It begins by detailing how to download and visualize the dataset using PyTorch and matplotlib and then transitions into training models in batches using PyTorch's DataLoader. The article describes the creation of three different models, demonstrating how changes to the model affect performance. Early models implement basic logistic regression, while later iterations incorporate a hidden layer and the ReLU activation function to improve accuracy, reaching up to 95% on the validation dataset. The training process is visualized through plots showing loss and accuracy improvements over epochs, emphasizing the importance of model architecture and hyperparameters in achieving high performance.