What is dimensionality reduction and why is it important in machine learning? How do techniques like PCA (Principal Component Analysis) and t-SNE reduce the number of features in a dataset? How does dimensionality reduction help improve model performance and reduce overfitting? In which types of problems is dimensionality reduction most useful? What are the limitations and challenges of applying dimensionality reduction techniques?