Natural Language Processing with NLTK and PYTHON

Module 1 – Introduction to NLP and NLTK

About NLP and its Applications

About Natural Language Toolkit

Getting Started with NLTK

NLTK installation

Loading Book

Module 2 – Introduction to Basics Python

List, Tuple and Dictionary

String Methods

Making Decisions and Taking Control

Conditionals, Operating on Every Element, Nested Code Blocks, Looping with Conditions

Module 3 – Operations on Text Data

Searching for Text

Counting Vocabulary

Texts as Lists of Word

List, Indexing list, variables, strings

Frequency Distributions

Fine-Grained Selection of Words

Collocations

Counting Other Things

Module 4 – Using NLTK Corpora for NLP

Accessing Text Corpora and Lexical Resources

Gutenberg Corpus

Gutenberg Corpus

Web and Chat Text Corpus

Brown Corpus

Reuters Corpus

Inaugural Address Corpus

Create and Load your own Corpus

Module 5 – Lexical Resources

Lexical Resources

Wordlist Corpora

Stopword

Name Corpus

Comparative Wordlists

WordNet

Module 6 – Text Data Preprocessing

Stop word removal

Tokenization: Word Tokenization and Sentence Tokenization

Stemmers

Lemmatization

Part-of-Speech Tagging

Feature Extraction Techniques

Bag of Words: CountVectorizer and TfidfVectorizer

Module 7 – Process Web Text Data

Processing Raw Text

Accessing Text from the Web

Dealing with HTML

Processing RSS Feeds

Reading Local Files

Module 8 – Regular Expressions

Regular Expressions for Detecting Word Patterns

Regular Expressions for Extracting Word Pieces

Regular Expressions for Tokenization

Regular Expressions for Stemming

Module 9 – Machine Learning for NLP

What is Machine Learning?

Types of Machine Learning

Data Pre-processing

Classification Algorithms: Naive Bayes, SVM and KNN

Module 10 – Model Building

Overview of Text Classification

Learning to Classify Text

Text Similarity

Text Clustering

Deep Learning for NLP

Module 11 – Hands on for NLP based Projects

Gender Identification

Document Classification

Sentiment Analysis: Twitter Sentiment Analysis

Text Summarization

Spam Detection