The document discusses linear regression, which is a simple machine learning algorithm that lays the foundation for other algorithms. It finds the relationship between one or more independent variables and a continuous dependent variable. The objective is to find the "best fit line" that minimizes the residual sum of squares between predicted and actual values. Gradient descent is used as the optimization algorithm to iteratively adjust parameters and reduce the cost function until convergence is reached. Root mean squared error and R2 score are common metrics used to evaluate model performance.