Natural Language Processing (NLP) is the most interesting subfield of data science. It offers powerful ways to interpret and act on spoken and written language. It’s used to help deal with customer support enquiries, analyse how customers feel about a product, and provide intuitive user interfaces. If you wish to build high performing day-to-day apps by leveraging NLP, then go for this course.

This course teaches you to write applications using one of the popular data science concepts, NLP. You will begin with learning various concepts of natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis. You will learn all of these through practical demonstrations, clear explanations, and interesting real-world examples. This course will give you a versatile range of NLP skills, which you will put to work in your own applications.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Natural Language Processing in Practice, will help you gain NLP skills by practical demonstrations, clear explanations, and interesting real-world examples. It will give you a versatile range of deep learning and NLP skills that you can put to work in your own applications.

The second course, Developing NLP Applications Using NLTK in Python, course is designed with advanced solutions that will take you from newbie to pro in performing natural language processing with NLTK. You will come across various concepts covering natural language understanding, natural language processing, and syntactic analysis. It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis.