Overview What You Will Learn Curriculum Instructor

Course Overview

Natural Language Processing (NLP) in the field of AI where a machine learns to decipher the human language, both text, and speech and derive some valuable information. Siri or Alexa is a popular example of NLP which interprets Speech Data. A Chatbot or a Virtual Assistant, or an Autocorrect or Auto-Completion of Text, or a Resume Scanner used by the recruiters are some examples where a machine works on interpreting Text data. 

We will begin with a detailed introduction to Natural Language Processing, its real-life applications. We will then move on to Emotion Detection techniques using TextBlob. We will get to know what TextBlob is, how to install and work with it. Moving forward, we will learn how to work with text data. Here, we will discover NLTK. Proceeding ahead, we will get to know about Word2Vec analysis and the concept of visualization. Then, we will explore the Sentiment Analysis Model. We will also work on a huge Movie Reviews Dataset published by Stanford to Build your very own NLP model for Sentiment Analysis. 

By the end of this course, you will have learned how a machine is trained to read and understand human language. Let’s begin!

What You Will Learn

  • Work with Text Data using Natural Language Tool Kit
  • Load and Process Text data using NLTK
  • Get an understanding of Regular Expression
  • StopWords
  • Stemming
  • Word2Vec
  • TextBlob
  • Analyze Text to discover Sentiments

Program Curriculum

  • What is NLP?
  • Applications of NLP
  • Installations
  • $7 Million Cybersecurity Scholarship by EC-Council

  • Introduction
  • What is TextBlob?
  • TextBlob Installation
  • Sentiment Analysis using TextBlob
  • TextBlob Polarity and Subjectivity
  • Chapter 2 Quiz

  • Introduction
  • Dataset
  • Load Dataset
  • Data Preparation
  • Natural Language Toolkit
  • Chapter 3 Quiz

  • Visualization
  • Word Frequency Distribution
  • Word Cloud
  • Scattertext
  • Chapter 4 Quiz

  • Introduction
  • Logistic Regression Model
  • Random Forest Model
  • Save and Load Model
  • Chapter 5 Quiz

  • Code Refactoring
  • Model Deployment
  • Epilogue
Load more modules

Instructor

Oindrila Sen

Oindrila Sen data enthusiast who has 10+ years of experience in analyzing, visualizing and working with Data. She is a Data Scientist who is currently working on a Customer Retention project for her client. She works in collecting, cleaning, extracting new features and Visualizing Text Data to get an insight. She also publishes her work on different platforms. She is passionate about Data and NLP since a text can tell you a whole new story.

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