Book Image

Python Natural Language Processing

Book Image

Python Natural Language Processing

Overview of this book

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.
Table of Contents (13 chapters)

Choose your area

After reading all the chapters, you might know enough to decide what you like. Do you want to build core ML stuff? Do you like to work on frameworks such as Hadoop, Spark, and so on? Are you keen on designing architecture? Do you want to contribute to visualization? Think and choose.

You can choose any area from data science or you can be a part of the whole data science product development life cycle. I want to give my example. I have worked with mid-size and start-up organizations. So far, I have had the freedom to explore various areas related to data science, such as proposing a data science product and releasing the product. I used to propose a new product after doing an analysis of business opportunities. I always validate my product proposal by thinking that if we were to make this product, then our end users would use it and, in return, the company that...