Learn to build expert NLP and machine learning projects using NLTK and other Python libraries
Key Features:
- Break text down into its component parts for spelling correction, feature extraction, and phrase transformation
- Work through NLP concepts with simple and easy-to-follow programming recipes
- Gain insights into the current and budding research topics of NLP
Book Description:
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 Hardeniya
- Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins
- Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur
What You Will Learn:
- The scope of natural language complexity and how they are processed by machines
- Clean and wrangle text using tokenization and chunking to help you process data better
- Tokenize text into sentences and sentences into words
- Classify text and perform sentiment analysis
- Implement string matching algorithms and normalization techniques
- Understand and implement the concepts of information retrieval and text summarization
- Find out how to implement various NLP tasks in Python
Who 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.
Module 1Nitin Hardeniya is a data scientist with over 4 years of experience working with companies such as Fidelity, Groupon, and [24]7-inc. He has worked on a variety of business problems across different domains, holds a master’s degree in Computational Linguistics from IIIT-H, and is the author of five patents in the field of customer experience. He is passionate about language processing and large unstructured data. Nitin has been using Python for almost 5 years in his day-to-day work and believes that Python could be a single-point solution to most of the problems related to data science.Module 2Jacob Perkins is the author of Python Text Processing with NLTK 2.0 and a contributor to the Bad Data Handbook. He is the CTO and co-founder of Weotta, a natural language based search engine for local entertainment. He created http
//text-processing.com, which demos NLTK functionality and provides natural language processing APIs. Jacob also writes about natural language processing and Python programming at http
//streamhacker.com and you can follow him on Twitter at @japerk.Module 3Deepti Chopra is an Assistant Professor at Banasthali University. Her primary areas of research are computational linguistics, Natural Language Processing, and artificial intelligence. She is also involved in the development of MT engines for English to Indian languages. Deepti has several journal and conference publications and also serves on the program committees of several conferences and journals. Nisheeth Joshi works as an Associate Professor at Banasthali University. His areas of interest include computational linguistics, Natural Language Processing, and artificial intelligence. He is also actively involved in the development of MT engines for English to Indian languages. He is one of the experts empaneled with the TDIL Programme’s Department of Information Technology for the Goverment of India, which is a premier organization that oversees Language Technology Funding and Research in India. He has several journal and conference publications and also serves on the program committees and editorial boards of several conferences and journals.Iti Mathur is an Assistant Professor at Banasthali University. Her areas of interest are computational semantics and ontological engineering. Besides this, she is also involved in the development of MT engines for English to Indian languages. She is one of the experts empaneled with TDIL Programme’s Department of Electronics and Information Technology for the Government of India, which is a premier organization that oversees Language Technology Funding and Research in India. Iti has publications in several journals and conferences and also serves on the program committees and editorial boards of several conferences and journals.
