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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, including resizing images and normalizing pixel values using TensorFlow's ImageDataGenerator. A CNN model is constructed in TensorFlow/Keras, consisting of convolutional layers, max pooling layers, a flattening layer, and dense layers. The model uses the 'adam' optimizer and 'binary_crossentropy' loss function. The model is trained using the preprocessed data. The training process is monitored using metrics like accuracy. Finally, the trained model can be used to classify images. TensorFlow simplifies the process of building and training image classification models. The article provides a practical example of image classification using TensorFlow.