How does a neural network differ from a traditional machine learning algorithm, and what types of problems are neural networks well-suited for?

A neural network differs from traditional machine learning algorithms by mimicking the structure and function of the human brain, utilizing interconnected layers of neurons to process complex data. Traditional machine learning algorithms rely on predefined features and mathematical models. Neural networks, on the other hand, can automatically learn and extract features from raw data, making them well-suited for tasks involving pattern recognition, image and speech recognition, natural language processing, and complex nonlinear relationships.