Dive into a comprehensive tutorial on neural networks in Python using TensorFlow and Keras. Learn the fundamentals of neural network architecture, including input layers, hidden layers, and output layers. Explore key concepts such as hyperparameters, batch size, learning rate, optimizers, activation functions, and dropout. Gain practical experience through coding examples that progress from simple linear classifications to complex multi-label tasks. Discover how to load and process data, build and fit neural networks, and evaluate model performance. Follow along with hands-on exercises, including classifying quadratic data, clustering, and predicting single data points. By the end of this tutorial, acquire the essential knowledge and skills to start implementing neural networks for various classification tasks in Python.