Answer: Statements I and III.

## Explanation **Correct Answer: C (Statements I and III)** **Statement I is correct:** Neural networks and deep learning are indeed well-suited for complex pattern recognition tasks such as image recognition, speech recognition, and natural language processing. These applications involve processing high-dimensional data with complex patterns that traditional methods struggle to capture. **Statement II is incorrect:** Single variable ordinary least squares regression is a simple linear modeling technique that doesn't require the complexity of neural networks. Traditional statistical methods are more appropriate and efficient for such straightforward linear relationships. **Statement III is correct:** Neural networks and deep learning excel at modeling non-linear relationships and complex interactions among multiple features. Their ability to learn hierarchical representations makes them particularly effective for capturing intricate patterns in data with many interacting variables. Therefore, only Statements I and III accurately describe the strengths of neural networks and deep learning.

Author: LeetQuiz Editorial Team