Natural Language Processing with Python

Course Overview

Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed.

This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language.  You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience.

At the end of the training, participants will be able to:

  1. Explain the basics of Natural Language Processing in the most popular Python Library: NLTK
  2. Adapt techniques to access or modify some of the most common file types
  3. Using I python notebooks, master the art of step by step text processing
  4. Gain insight into the ‘Roles’ played by an NLP Engineer
  5. Interpret Bag of Words Modelling and Tokenization of Text.
  6. Utilize n-Gram Models to model and analyze the Bag of words from Corpus
  7. Interpret Latent Semantic Analysis and its usage in the processing of context-aware Semantic Content.
  8. Work with real-time data
  9. Interpret Sentiment Analysis one of the most interesting applications of Natural Language Processing
  10. Gain expertise to handle business in the future, living the present

Pre-requisite

  1. Working knowledge in Python
  2. Good Understanding of Machine Learning Concept

Duration

3 days

Course Outline

  1. Introduction
  2. What is AI?
  3. Philosophy of AI
  4. Goals
  5. What contributes to AI?
  6. Programming without and with AI
  7. Applications of AI
  8. Types of Intelligence
  9. Agents and Environments
  1. Why Python for ML?
  2. Anaconda – Overview and Installation
  3. Using Jupyter Notebook
  4. Variables
  5. Comprehension
  6. Functions and Modules
  7. Concept of Classes and Objects
  1. NumPy – Array manipulation
  2. Pandas – Data Analytics
  3. Matplotlib and Seaborn – Data Visualization
  4. Sklearn – Machine Learning (Regression and Classification)
  1. Introduction
  2. History of NLP
  3. Study of Human Languages
  4. Ambiguity and Uncertainty in Language
  5. Phases
  1. Overview of Text Mining
  2. Need of Text Mining
  3. Using NLP
  4. Applications of Text Mining
  5. OS Module
  6. Reading and Writing the files
  7. Setting the NLTK environment
  8. Accessing the NLTK corpora
  1. Tokenization
  2. Frequency Distribution
  3. Different types of Tokenizers
  4. Stemming
  5. Lemmatization
  6. Bigrams, Trigrams and Ngrams
  7. Stopwords
  8. POS Tagging
  9. Named Entity Recognition
  1. Regular Expressions
  2. Syntax Trees
  3. Chunking and Chinking
  4. Context Free Grammars (CFG)
  5. Automatic Text Paraphrasing
  1. What is Text Classification?
  2. How does Text Classification works?
  3. Applications
  4. Usecases
  1. What is Text Summarization?
  2. Steps involved in Summarization
  3. Applications

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