Python Natural Language Processing

Unsupervised learning

In this type of ML, we will provide an unlabeled dataset as input to the ML algorithm. So, our algorithm doesn't get any feedback on what is correct or not. It has to learn by itself the structure of the data to solve a given task. It is harder to use an unlabeled dataset, but it's more convenient because not everyone has a perfectly labeled dataset. Most data is unlabeled, messy, and complex.

Suppose we are trying to develop a summarization application. We probably haven't summarized the documents corresponding to the actual document. Then, we will use raw and the actual text document to create a summary for the given documents. Here, the machine doesn't get any feedback as to whether the summary generated by the ML ...