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This article explains how to build and run an image classification model using TensorFlow. Image classification involves feature extraction and classifier construction, often using Convolutional Neural Networks (CNNs). Data preprocessing is crucial; the example uses ImageDataGenerator to rescale images. A CNN model is built using TensorFlow's Keras API, consisting of convolutional layers, max pooling layers, a flattening layer, and dense layers. The model uses 'adam' optimizer and 'binary_crossentropy' loss function. The model is trained using the preprocessed data for 10 epochs. The training history is then obtained for evaluation. TensorFlow simplifies the process of creating functional image classification models. The example demonstrates a binary classification scenario. This approach allows for practical application of machine learning to image classification tasks.