Hands On Natural Language Processing (NLP) using Python

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: What is NLP?

Lecture 2: Getting the Course Resources

Lecture 3: Getting the Course Resources – Text

Chapter 2: Getting the required softwares

Lecture 1: Installing Anaconda Python

Lecture 2: Installing Anaconda Python – Text

Lecture 3: A tour of Spyder IDE

Lecture 4: How to take this course?

Chapter 3: Python Crash Course

Lecture 1: Variables and Operations in Python

Lecture 2: Conditional Statements

Lecture 3: Introduction to Loops

Lecture 4: Loop Control Statements

Lecture 5: Python Data Structures – Lists

Lecture 6: Python Data Structures – Tuples

Lecture 7: Python Data Structures – Dictionaries

Lecture 8: Console and File I/O in Python

Lecture 9: Introduction to Functions

Lecture 10: Introduction to Classes and Objects

Lecture 11: List Comprehension

Chapter 4: Regular Expressions

Lecture 1: Introduction to Regular Expressions

Lecture 2: Finding Patterns in Text Part 1

Lecture 3: Finding Patterns in Text Part 2

Lecture 4: Substituting Patterns in Text

Lecture 5: Shorthand Character Classes

Lecture 6: Character Ranges – Text

Lecture 7: Preprocessing using Regex

Chapter 5: Numpy and Pandas

Lecture 1: Introduction to Numpy

Lecture 2: Introduction to Pandas

Chapter 6: NLP Core

Lecture 1: Installing NLTK in Python

Lecture 2: Tokenizing Words and Sentences

Lecture 3: How tokenization works? – Text

Lecture 4: Introduction to Stemming and Lemmatization

Lecture 5: Stemming using NLTK

Lecture 6: Lemmatization using NLTK

Lecture 7: Stop word removal using NLTK

Lecture 8: Parts Of Speech Tagging

Lecture 9: POS Tag Meanings

Lecture 10: Named Entity Recognition

Lecture 11: Text Modelling using Bag of Words Model

Lecture 12: Building the BOW Model Part 1

Lecture 13: Building the BOW Model Part 2

Lecture 14: Building the BOW Model Part 3

Lecture 15: Building the BOW Model Part 4

Lecture 16: Text Modelling using TF-IDF Model

Lecture 17: Building the TF-IDF Model Part 1

Lecture 18: Building the TF-IDF Model Part 2

Lecture 19: Building the TF-IDF Model Part 3

Lecture 20: Building the TF-IDF Model Part 4

Lecture 21: Understanding the N-Gram Model

Lecture 22: Building Character N-Gram Model

Lecture 23: Building Word N-Gram Model

Lecture 24: Understanding Latent Semantic Analysis

Lecture 25: LSA in Python Part 1

Lecture 26: LSA in Python Part 2

Lecture 27: Word Synonyms and Antonyms using NLTK

Lecture 28: Word Negation Tracking in Python Part 1

Lecture 29: Word Negation Tracking in Python Part 2

Chapter 7: Project 1 – Text Classification

Lecture 1: Getting the data for Text Classification

Lecture 2: Getting the data for Text Classification – Text

Lecture 3: Importing the dataset

Lecture 4: Persisting the dataset

Lecture 5: Preprocessing the data

Lecture 6: Transforming data into BOW Model

Lecture 7: Transform BOW model into TF-IDF Model

Lecture 8: Creating training and test set

Lecture 9: Understanding Logistic Regression

Lecture 10: Training our classifier

Lecture 11: Testing Model performance

Lecture 12: Saving our Model

Lecture 13: Importing and using our Model

Chapter 8: Project 2 – Twitter Sentiment Analysis

Lecture 1: Setting up Twitter Application

Lecture 2: Initializing Tokens

Lecture 3: Client Authentication

Lecture 4: Fetching real time tweets

Lecture 5: Loading TF-IDF Model and Classifier

Lecture 6: Preprocessing the tweets

Lecture 7: Predicting sentiments of tweets

Lecture 8: Plotting the results

Chapter 9: Project 3 – Text Summarization

Lecture 1: Understanding Text Summarization

Lecture 2: Fetching article data from the web

Lecture 3: Parsing the data using Beautiful Soup

Lecture 4: Preprocessing the data

Lecture 5: Tokenizing Article into sentences

Lecture 6: Building the histogram

Lecture 7: Calculating the sentence scores

Lecture 8: Getting the summary

Chapter 10: Word2Vec Analysis

Lecture 1: Understanding Word Vectors

Lecture 2: Importing the data

Lecture 3: Preparing the data