Neural network training is a systematic process that transforms raw data into intelligent predictions. The process begins with data preparation, where we clean and format our dataset. Then we design the network architecture with input, hidden, and output layers. During training, the network learns patterns by adjusting weights and biases through backpropagation. Finally, we evaluate the model's performance on test data.