Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

Summary

In this chapter, you learned about different types of machine learning techniques, such as supervised and unsupervised learning. Various kinds of supervised learning algorithms, such as K-Nearest Neighbor and a Naive Bayes classifier have been explored. Moreover, different kinds of sampling techniques for splitting a given dataset into training and validation sets have also been elucidated with examples. This chapter focused mainly on developing machine learning models using features extracted from text data.

As you progressed through the chapter, you were introduced to various metrics used for evaluating the performance of these models. Finally, we covered the process of saving a model on the hard disk and loading it back to the memory for future use.

In the next chapter, you will learn several techniques with which data can be collected from various sources.