Natural Language Processing with Python

Live Online (VILT) & Classroom Corporate Training Course

Natural Language Processing with Python course will take you through the essentials of text processing all the way up to classifying texts using Machine Learning algorithms.
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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.

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Objectives

At the end of Natural Language Processing with Python training course, participants will be able to

  • Explain the basics of Natural Language Processing in the most popular Python Library: NLTK
  • Adapt 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
  • Interpret Bag of Words Modelling and Tokenization of Text
  • Utilize n-Gram Models to model and analyze the Bag of words from Corpus
  • Interpret Latent Semantic Analysis and its usage in the processing of context-aware Semantic Content
  • Work with real-time data
  • Interpret Sentiment Analysis one of the most interesting applications of Natural Language Processing
  • Gain expertise to handle business in the future, living the present
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Prerequisites

  • Working knowledge in Python
  • Good Understanding of Machine Learning Concept
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Course Outline

  • Introduction
  • What is AI?
  • Philosophy of AI
  • Goals
  • What contributes to AI?
  • Programming without and with AI
  • Applications of AI
  • Types of Intelligence
  • Agents and Environments

  • Why Python for ML?
  • Anaconda – Overview and Installation
  • Using Jupyter Notebook
  • Variables
  • Comprehension
  • Functions and Modules
  • Concept of Classes and Objects

  • NumPy – Array manipulation
  • Pandas – Data Analytics
  • Matplotlib and Seaborn – Data Visualization
  • Sklearn – Machine Learning (Regression and Classification)

  • Introduction
  • History of NLP
  • Study of Human Languages
  • Ambiguity and Uncertainty in Language
  • Phases

  • Overview of Text Mining
  • Need of Text Mining
  • Using NLP
  • Applications of Text Mining
  • OS Module
  • Reading and Writing the files
  • Setting the NLTK environment
  • Accessing the NLTK corpora

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

  • Regular Expressions
  • Syntax Trees
  • Chunking and Chinking
  • Context Free Grammars (CFG)
  • Automatic Text Paraphrasing

  • What is Text Classification?
  • How does Text Classification works?
  • Applications
  • Usecases

  • What is Text Summarization?
  • Steps involved in Summarization
  • Applications
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