This chapter provides a comprehensive exploration of Multilayer Perceptron (MLP) Neural Networks, beginning with the fundamental architecture of neurons and progressing to complex network structures. It details the error backpropagation algorithm as the cornerstone of MLP training, explaining both forward and backward phases of weight adjustment. The chapter demonstrates practical implementation through extensive Python code examples, covering neural network initialization, forward propagation, and gradient descent-based training. Significant attention is given to MLP applications in classification tasks, particularly in medical diagnostics using diabetes datasets, with comparisons of various machine learning algorithms. The chapter also addresses critical training considerations including over-parameterization and over-training, providing practical strategies for optimal network configuration. Throughout, the content maintains a strong practical focus with executable code snippets for system estimation, classification, and performance evaluation.