What to read after Natural Language Processing: Python and NLTK?

Learn to build expert NLP and machine learning projects using NLTK and other Python libraries

About This BookBreak text down into its component parts for spelling correction, feature extraction, and phrase transformationWork through NLP concepts with simple and easy-to-follow programming recipesGain insights into the current and budding research topics of NLPWho This Book Is For

If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable.

What You Will LearnThe scope of natural language complexity and how they are processed by machinesClean and wrangle text using tokenization and chunking to help you process data betterTokenize text into sentences and sentences into wordsClassify text and perform sentiment analysisImplement string matching algorithms and normalization techniquesUnderstand and implement the concepts of information retrieval and text summarizationFind out how to implement various NLP tasks in PythonIn Detail

Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages.

The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy.

The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods.

The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products:

NTLK essentials by Nitin HardeniyaPython 3 Text Processing with NLTK 3 Cookbook by Jacob PerkinsMastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti MathurStyle and approach

This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.