Learn how to implement and train a U-Net model for medical polyp image segmentation using TensorFlow and Keras in this comprehensive tutorial. Dive into data preprocessing, U-Net architecture construction, model training, and evaluation. Begin by loading and preparing the polyp dataset, including image resizing and mask conversion. Explore the U-Net model architecture, building convolutional layers for both encoder and decoder parts. Train the model using optimized parameters and callbacks for improved performance. Finally, evaluate the trained model on test data and visualize predicted segmentation masks. Gain hands-on experience in medical image analysis and deep learning techniques for precise polyp detection and segmentation.