So far, we have looked at how various machine learning algorithms work at a high level. In this section, we will understand how deep learning differs from machine learning.
One of the key attributes of a machine learning task is that the inputs are given by the analyst or data scientist. Quite often, feature engineering plays a key role in improving the accuracy of the model. Moreover, if the input dataset is an unstructured one, feature engineering gets a lot more tricky. More often than not, it boils down to the knowledge of individual in deriving relevant features to build a more accurate model.
For example, let's imagine a scenario where, given a set of words in a sentence, we ...
