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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 and normalization using TensorFlow's ImageDataGenerator. A CNN model is constructed using 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 prepared dataset and its accuracy is evaluated. The training process involves specifying the number of epochs. The article demonstrates a practical implementation of image classification with TensorFlow. The resulting model can classify images based on learned features. TensorFlow simplifies the process of building and training image classification models.