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- What is NLP?
- Getting the Course Resources
- Getting the Course Resources - Text
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 499 ₹3,099
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction to the Course
Getting the required softwares
- Installing Anaconda Python
- Installing Anaconda Python - Text
- A tour of Spyder IDE
- How to take this course?
Python Crash Course
- Variables and Operations in Python
- Conditional Statements
- Introduction to Loops
- Loop Control Statements
- Python Data Structures - Lists
- Python Data Structures - Tuples
- Python Data Structures - Dictionaries
- Console and File I/O in Python
- Introduction to Functions
- Introduction to Classes and Objects
- List Comprehension
- Test Your Skills
Regular Expressions
- Introduction to Regular Expressions
- Finding Patterns in Text Part 1
- Finding Patterns in Text Part 2
- Substituting Patterns in Text
- Shorthand Character Classes
- Character Ranges - Text
- Preprocessing using Regex
- Test Your Skills
Numpy and Pandas
- Introduction to Numpy
- Introduction to Pandas
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 Part 1
- Building the BOW Model Part 2
- Building the BOW Model Part 3
- Building the BOW Model Part 4
- Text Modelling using TF-IDF Model
- Building the TF-IDF Model Part 1
- Building the TF-IDF Model Part 2
- Building the TF-IDF Model Part 3
- Building the TF-IDF Model Part 4
- Understanding the N-Gram Model
- Building Character N-Gram Model
- Building Word N-Gram Model
- Understanding Latent Semantic Analysis
- LSA in Python Part 1
- LSA in Python Part 2
- Word Synonyms and Antonyms using NLTK
- Word Negation Tracking in Python Part 1
- Word Negation Tracking in Python Part 2
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
Conclusion
- Where you go from here?
Articles
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