About the course
Explore various NLP tasks while enhancing your Python skills in real-world scenarios
Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned 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.This course will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK. You will sequentially be guided through applying machine learning tools to develop various models. We’ll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Text Summarization, and Anaphora Resolution.
The code bundle for the video course is available at - https://github.com/PacktPublishing/Mastering-Natural-Language-Processing-with-Python-Video
Style and Approach
This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.
What You Will Learn
- Implement string matching algorithms and normalization techniques
- Implement statistical language modeling techniques
- Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
- Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
- Develop an NER-based system and understand and apply the concepts of semantic analysis
- Understand and implement the concepts of Information Retrieval and text summarization
- Develop a Discourse Analysis System and Anaphora Resolution based system
Deepti Chopra is an Assistant Professor at Banasthali University. Her primary area of research is computational linguistics, Natural Language Processing, and artificial intelligence. She is also involved in the development of MT engines for English to Indian languages. She has several publications in various journals and conferences and also serves on the program committees 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 program, Department of Electronics and Information Technology (DeitY), Govt. of India, a premier organization that oversees Language Technology Funding and Research in India. She has several publications in various journals and conferences and also serves on the program committees and editorial boards of several conferences and journals.
Nisheeth Joshi is an associate professor and a researcher at Banasthali University. He has also done a PhD in Natural Language Processing. He is an expert with the TDIL Program, Department of IT, Government of India, the premier organization overseeing language technology funding and research in India. He has several publications to his name in various journals and conferences, and also serves on the program committees and editorial boards of several conferences and journals.
Implementing Supply Chain Management