This segment explains the use of linear and step activation functions in a single neuron model for regression and classification tasks, respectively. It details how a neuron computes a weighted sum of inputs, adds a bias, and applies an activation function to produce an output, illustrating the types of functions (linear for regression, step for classification) a single neuron can compute and their limitations in terms of input and output dimensionality. A single neuron in a neural network computes a weighted sum of its inputs, adds a bias, and applies an activation function (linear for regression, step for classification). Training involves finding optimal weights and bias using a loss function (e.g., sum of squared errors) and gradient descent to minimize error and best fit the data. The input dimension determines the number of weights. This segment focuses on the training process of a single neuron model. It explains how the choice of activation function (linear or step) depends on the nature of the output (continuous or binary), and how the input dimensionality determines the number of weights in the model. The concept of a loss function is introduced, highlighting its role in quantifying the model's error in representing the data, setting the stage for the gradient descent method discussed in the subsequent video.