Duration
2 days
What you'll learn
- Understand the various concepts of natural language processing along with their implementation
- Build natural language processing based applications
- Learn about the different modules available in Python for NLP
- Create personal text summarizer
Who this course is for
- Anyone willing to start a career in data science and natural language processing
- Anyone willing to learn the concepts of natural language processing by implementing them
- Anyone willing to learn Sentiment Analysis
Course Outline
Introduction to the Course
- What is NLP?
- Installing Anaconda Python
- Installing Anaconda Python - Text
- A tour of Spyder IDE Regular Expressions
- Introduction to Regular Expressions
- Finding Patterns in Text
- Substituting Patterns in Text
- Shorthand Character Classes
- Character Ranges - Text
- Preprocessing using Regex
NLP Core
- Installing NLTK in Python
- Tokenizing Words and Sentences
- How tokenization works? - Text
- Introduction to Stemming and Lemmatization
- Stemming using NLTK
- Lemmatization using NLTK
- Stop word removal using NLTK
- Parts Of Speech Tagging
- POS Tag Meanings
- Named Entity Recognition
- Text Modelling using Bag of Words Model
- Building the BOW Model
- Text Modelling using TF-IDF Model
- Understanding the N-Gram Model
- Understanding Latent Semantic Analysis
- Word Synonyms and Antonyms using NLTK
- Word Negation Tracking in Python
Project 1 - Text Classification
- Getting the data for Text Classification
- Getting the data for Text Classification - Text
- Importing the dataset
- Persisting the dataset
- Preprocessing the data
- Transforming data into BOW Model
- Transform BOW model into TF-IDF Model
- Creating training and test set
- Understanding Logistic Regression
- Training our classifier
- Testing Model performance
- Saving our Model
- Importing and using our Model
Project 2 - Twitter Sentiment Analysis
- Setting up Twitter Application
- Initializing Tokens
- Client Authentication
- Fetching real time tweets
- Loading TF-IDF Model and Classifier
- Preprocessing the tweets
- Predicting sentiments of tweets
- Plotting the results
Project 3 - Text Summarization
- Understanding Text Summarization
- Fetching article data from the web
- Parsing the data using Beautiful Soup
- Preprocessing the data
- Tokenizing Article into sentences
- Building the histogram
- Calculating the sentence scores
- Getting the summary
Word2Vec Analysis
- Understanding Word Vectors
- Importing the data
- Preparing the data
- Training the Word2Vec Model
- Testing Model Performance
- Improving the Model
- Exploring Pre-trained Models
Programme scope, delivery arrangements and suitability are confirmed during enquiry.