Natural Language Processing with Python Certification

Secure enrollment now

Syllabus

Introduction to Text Mining and NLP

 

•    Overview of Text Mining
•    Need of Text Mining
•    Natural Language Processing (NLP) in Text Mining
•    Applications of Text Mining
•    OS Module
•    Reading, Writing to text and word files
•    Setting the NLTK Environment
•    Accessing the NLTK Corpora

 

Extracting, Cleaning and Pre-processing Text

 

•    Tokenization
•    Frequency Distribution
•    Different Types of Tokenizers
•    Bigrams, Trigrams & Ngrams
•    Stemming
•    Lemmatization
•    Stopwords
•    POS Tagging
•    Named Entity Recognition

 

Analyzing Sentence Structure

 

•    Syntax Trees
•    Chunking
•    Chinking
•    Context Free Grammars (CFG)
•    Automating Text Paraphrasing

 

Text Classification - I

 

•    Machine Learning: Brush Up
•    Bag of Words
•    Count Vectorizer
•    Term Frequency (TF)
•    Inverse Document Frequency (IDF)

 

Text Classification - II

 

•    Converting text to features and labels
•    Multinomial Naiive Bayes Classifier
•    Leveraging Confusion Matrix


Complimentary sessions on communication presentation and leadership skills.

Benefits from the course

Mode of Teaching

Live Interactive

Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users.

NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.

After completing this NLP training in Python, you will be able to:

  • Learn basics of Natural Language Processing in the most popular Python Library: NLTK
  • Learn techniques to access or modify some of the most common file types
  • Using I python notebooks, master the art of step by step text processing
  • Gain insight into the 'Roles' played by an NLP Engineer
  • Learn about Bag of Words Modelling and Tokenization of Text.
  • Use n-Gram Models to model and analyze the Bag of words from Corpus
  • Learn about converting text to vector using word frequency count, tf-idf etc.
  • Learn about Latent Semantic Analysis and its usage in the processing of context-aware Semantic Content.
  • Work with real-time data
  • Learn in detail about Sentiment Analysis one of the most interesting applications of Natural Language Processing
  • Gain expertise to handle business in future, living the present.

 

Prerequisite:

 

  • Understanding of Machine Learning concepts and Python programming
  • As a goodwill gesture, a complimentary self-paced course will be provided in your LMS on Python to brush up on your Python Skills.

Prerequisites

  • Understanding of Machine Learning concepts and Python programming
  • As a goodwill gesture, a complimentary self-paced course will be provided in your LMS on Python to brush up on your Python Skills.

Course Duration:

36 Hours

Class Hours:

2 Hours Day time slots or 3 Hours week end Slots (May change)

Video Clip