Text Analytics with Python

Chapter 1: Natural Language Basics.- Chapter Goal: Introduces the readers to the basics of NLP and Text processing No of pages: 40 - 50 Sub -Topics 1. Language Syntax and Structure 2. Text formats and grammars 3. Lexical and Text Corpora resources 4. Deep dive into the Wordnet corpus 5. Parts of speech, Stemming and lemmatization Chapter 2: Python Refresher for Text Analytics Chapter Goal: A useful chapter for people who do not know python as well as for experienced people who can use it as a quick reference for useful commands and techniques for text processing using python No of pages: 30 - 35 Sub - Topics 1. Python data structures and constructs 2. Functions, conditionals and code flow 3. Handling strings with Python 4. Regular Expressions with Python 5. Quick glance into nltk, gensim and pattern Chapter 3: Text Processing Chapter Goal: This chapter covers all the techniques and capabilities needed for processing and parsing text into easy to understand formats. We also look at how to segment and normalize text. No of pages : 35 - 40 Sub - Topics: 1. Sentence and word tokenization 2. Text tagging and chunking 3. Text Parse Trees 3. Text normalization 4. Text spell checks and removal of redundant characters 5. Synonyms and Synsets
Chapter 4: Text Classification Chapter Goal: Introduces readers to the concept of classification as a supervised machine learning problem and looks at a real world example for classifying text documents No of pages: 40 - 45 Sub - Topics: 1. Classification basics 2. Types of classifiers 3. Feature generation of text documents 4. Types of feature generators 5. Building a text classifier on real world data 6. Evaluating Classifiers 7. Binary and multi-class classification models


Chapter 5: Text summarization and topic modeling Chapter Goal: Introduces the concepts of text summarization, n-gram tagging analysis and topic models to the readers and looks at some real world datasets and hands-on implementations on the same No of

Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company which is on a mission to make the world more connected and productive. He primarily works on Analytics, Business Intelligence, Application Development and building large scale Intelligent Systems. He received his master's degree in Information Technology from the International Institute of Information Technology, Bangalore with a focus on Data Science and Software Engineering. He is also an avid supporter of self-learning, especially Massive Open Online Courses and holds a Data Science Specialization from Johns Hopkins University on Coursera.
He has been an analytics practitioner for over 4 years now specializing in statistical, predictive and text analytics. He has also authored a couple of books on R and Machine Learning and occasionally reviews technical books and acts as a course beta tester for Coursera. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science and more recently, artificial intelligence and deep learning. In his spare time he loves reading, gaming and watching popular sitcoms and football.