About the course
Learn a practical viewpoint to understand and implement NLP solutions involving POS tagging, parsing, and much more
Have you ever faced challenges in understanding language and planning sentences while performing Natural Language Processing? Do you wish to overcome these problems and go beyond the basic techniques like bag-of-words?
Well, now you can. This course is designed with advanced solutions that will take you from newbie to pro in performing Natural Language Processing with NLTK. In this course, you will come across various concepts covering natural language understanding, Natural Language Processing, and syntactic analysis.
It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis.
By the end of this course, you will have all the knowledge you need to implement Natural Language Processing with Python.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Developing-NLP-Applications-Using-NLTK-in-Python
Style and Approach
The standalone solutions of this course will teach you how to efficiently perform Natural Language Processing in Python. It covers state-of-the-art techniques necessary for applications in NLP. Addressing your common and not-so-common pain points, this is a course that you must have on your library.
What You Will Learn
- Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP.
- Write your own POS taggers and grammars so that any syntactic analyses can be performed easily.
- Use the inbuilt chunker and create your own chunker to evaluate trained models.
- Create your own named entities using dictionaries to use inbuilt text classification algorithms.
- Combine various lessons and create applicable solutions that can be easily plugged into any of your real-life application problems.
Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining in various industry domains such as hospitality, banking, healthcare, and more. He has worked on many different NLP libraries such as Stanford CoreNLP, IBM's SystemText and BigInsights, GATE, and NLTK to solve industry problems related to textual analysis. He has also worked on analyzing social media responses for popular television shows and popular retail brands and products. He has also published a paper on sentiment analysis augmentation techniques in 2010 NAACL. he recently created an NLP pipeline/toolset and open sourced it for public use. Apart from academics and technology, Krishna has a passion for motorcycles and football. In his free time, he likes to travel and explore. He has gone on pan-India road trips on his motorcycle and backpacking trips across most of the countries in South East Asia and Europe.
Naresh Kumar has more than a decade of professional experience in designing, implementing, and running very-large-scale Internet applications in Fortune Top 500 companies. He is a full-stack architect with hands-on experience in domains such as ecommerce, web hosting, healthcare, big data and analytics, data streaming, advertising, and databases. He believes in open source and contributes to it actively. Naresh keeps himself up-to-date with emerging technologies, from Linux systems internals to frontend technologies. He studied in BITS-Pilani, Rajasthan with dual degree in computer science and economics.
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about nextgen technologies and innovative methodologies. He is also the author of the book Statistics for Machine Learning by Packt.
Text Mining with Machine Learning and Python [Video]