Introduction to NLP
Introduction
Welcome to the first module of natural language processing. Natural language processing, also referred to as text analytics, plays a very vital role in today’s era because of the sheer volume of text data that users generate around the world on digital channels such as social media apps, e-commerce websites, blog posts, etc. The first session of this module will take you through the following lectures:
- Industry applications of text analytics
- Understanding textual data
- Regular expressions
In Module-1 you’ll do lots of hands-on practice (especially while learning regular expressions), while Module-2 and Module-3 will focus more on concepts, algorithms and applications such as building POS taggers, NER systems, unsupervised analysis (twitter opinion mining), and finally semantics.
Please note that the terms ‘natural language processing’ and ‘text analytics’ will be used interchangeably in this course. However, they refer to the same entity.
Prerequisites
There are no prerequisites for this session other than knowledge of the previous courses on Statistics and ML.
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded. Note that graded questions are given on a separate page labelled ‘Graded Questions’ at the end of this session. The graded questions in this session will adhere to the following guidelines:
| First Attempt Marks | Second Attempt Marks | |
| Question with 2 Attempts | 10 | 5 |
| Question with 1 Attempt | 10 | 0 |