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)

Machine Learning

Machine learning refers to the process of comprehending the patterns present in a dataset. It helps machines to learn from any given data and produce appropriate results, without being programmed explicitly. Basically, machine learning algorithms are fed with large amounts of data that they can work on and build a model. This model is later used by businesses to generate solutions that help them with analysis and building strategies for the future.

Machine learning is further categorized into unsupervised and supervised learning. Let's understand these in detail in the next section.

Unsupervised Learning

Unsupervised learning is the method by which algorithms tend to learn patterns within data that is not labeled. Since labels (supervisors) are absent, it is referred to as unsupervised learning. In unsupervised learning, you provide the algorithm with the feature data and it learns patterns from the data on its own.

Unsupervised learning is further...