About NLTK: –

The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language.

Course Description: –

This course introduces Natural Language Processing (NLP) with the use of Natural Language Tool Kit (NLTK) and Python. Through practical approach, you will get hands-on experience with Natural language concepts and computational linguistics concepts. This course is designed for people interested in learning NLP from scratch. No prior knowledge of NLP techniques is assumed.

Course Outcome: –

On completion of this course, the students will be able to

• Understand the concepts of Natural Language Processing (NLP) along with Machine Learning.
• Understand the nature of text data and how to work with it using NLTK.
• Load and manipulate your text data.
• Hands on to code the NLP related project.
• Apply various concepts of NLP in other application areas.

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

Prerequisites: –

1.Basic of any programming knowledge.

2.Student should have good logical and reasoning skill

Duration & Timings :

Duration – 30 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

Access to Class Recordings.

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