Machine learning (ML) and deep learning (DL) are both subsets of artificial intelligence, but they differ in several key ways. ML uses a variety of algorithms like decision trees and support vector machines to make predictions based on data and often requires manual feature engineering. It works well with smaller datasets and is computationally less intensive, making it more interpretable and easier to implement for simpler problems. DL, a subset of ML, uses neural networks with many layers to automatically learn features from large amounts of unstructured data. This requires significant computational power and large datasets but excels in complex tasks like image and speech recognition. DL models often outperform traditional ML models in these tasks but are less interpretable and more resource-intensive. Mainly deep learning problems are black box because the decision making is not seen to us,as their thousands of parameters are their for simplest of neural networks but the interactions between them cannot be interpreted or traced easily