Firstly, we must understand that Deep learning is a subset of ML and there are key distinctions which students and many definitions online fail to make. For example, let us look at the first google search:
Okay, their definition is true in princple, however do not think it is a bit vague.
Anywyas, Deep learning means you are dealing with neural networks. neural networks are MLPs, CNNs, RNNs, Foundational Models, I don't know what else is out there but these are some of the examples. On the other hand, Machine learning are a bit more boring. They are very useful, many times more than Machine learning models. However, they consist of algorhtims which were finely made my statistications and data scientists such as the KNN algorithim, Linear regression, PCA, and many others. There are other aspects which the two differ on such as Trasnfer learning exists in Deep learning, feature extraction is necessary in Ml. However, they are many aspects they share. For example, both have some type of loss functions to adjust the internal paramaters called weights.
Secondly, it is important to note that ML is generally much more interptable than Deep learning. The definition of interptablility is xyz. Why does this matter? Well these two fields intersect with statistics and one of the areas which matters a lot is undertsanding the relationship between different features and our target variable. Let me elaborte : Neural Networks : We cannot understand why the output is what it is. We don't know anything about the neurons or the relationships. No statiscal inference or undertsnading is present. However, if for the same problem we used a linear regression, it is much more interpretable