Non-linear techniques refer to methods used in various fields, such as mathematics, statistics, and machine learning, where relationships between variables are not straight lines. Unlike linear techniques, which assume a constant rate of change, non-linear techniques can model complex behaviors and interactions. This allows for a more accurate representation of real-world phenomena, such as in economics or biological systems. In machine learning, non-linear techniques include algorithms like neural networks and decision trees, which can capture intricate patterns in data. These methods are particularly useful for tasks like image recognition and natural language processing, where relationships between inputs and outputs are often non-linear and multifaceted.