What this learning path covers
Module 1, NLTK Essentials, talks about all the preprocessing steps required in any text mining/NLP task. In this module, we discuss tokenization, stemming, stop word removal, and other text cleansing processes in detail and how easy it is to implement these in NLTK.
Module 2, Python 3 Text Processing with NLTK 3 Cookbook, explains how to use corpus readers and create custom corpora. It also covers how to use some of the corpora that come with NLTK. It covers the chunking process, also known as partial parsing, which can identify phrases and named entities in a sentence. It also explains how to train your own custom chunker and create specific named entity recognizers.
Module 3, Mastering Natural Language Processing with Python, covers how to calculate word frequencies and perform various language modeling techniques. It also talks about the concept and application of Shallow Semantic Analysis (that is, NER) and WSD using Wordnet.
It will help you understand and apply the concepts of Information Retrieval and text summarization.
