Neural networks are a specific class of machine learning (ML) models that are inspired by the structure and function of the human brain. They consist of interconnected layers of nodes (neurons) that process input data through weighted connections. The primary strength of neural networks lies in their ability to automatically learn feature representations, making them particularly effective in complex tasks such as image recognition, language processing, and game playing.
Other ML models, like decision trees, support vector machines (SVMs), or linear regression, are generally simpler and rely on manually defined features or mathematical transformations of the data. These models may be easier to interpret but may not perform as well on complex tasks, especially with large datasets.
The main difference lies in neural networks' ability to model highly nonlinear relationships and learn complex patterns directly from raw data, while traditional models often require more explicit feature engineering.