The article provides an introductory guide to building a Convolutional Neural Network (CNN) for image classification, focusing on practical implementation using Keras. It explains fundamental concepts of CNNs, such as the convolution operation, feature maps, pooling, flattening, and fully connected layers, all crucial for effective image processing. The CNN architecture is structured to classify images by detecting complex features, drawing inspiration from the human brain's feature detection capabilities. The guide further details the preprocessing steps, including image augmentation to prevent overfitting, and offers code snippets for setting up a CNN, emphasizing the importance of choosing appropriate parameters like filter size and activation functions. Additionally, it covers the process of training and testing the model, using image data generators to handle image input, and finally deploying the model to predict the classes of new images. The article concludes by encouraging further exploration of CNNs through additional resources like the Keras documentation.