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About Natural Language Processing (NLP) using Python Course

Why pursue Natural Language Processing (NLP)?

  • More than 80% of the data in this world is unstructured in nature, which includes text. You need text mining and Natural Language processing  (NLP) to make sense out of this data.

  • Natural Language Processing (NLP) helps you extract insights from emails of customers, their tweets, text messages.

  • Natural Language Processing (NLP) can power many applications, such as language translation, question answering systems, chatbots and document summarizers.

What would you learn in Natural Language Processing (NLP) with Python course?

  • Reading and working with text data using Python
  • Learn to use Regular Expressions to extract patterns from text
  • Text pre-processing using the NLTK and spaCy libraries
    • Parts of Speech Tagging (POS Tagging)
    • Named Entity Recognition (NER)
  • Text Normalization
    • Stemming
    • Lemmatization
  • Topic Modeling - Interpreting patterns from text
    • Latent Dirichlet Algorithm (LDA)
    • Latent Semantic Analysis (LSA)
  • Feature Engineering for text
    • Bag-of-words and TF-IDF
    • Singular Value Decomposition (SVD)
    • Word Embeddings (Word2vec and GloVe)
  • How to identify topics in text - Topic Modeling
  • Text classification
  • Deep learning for NLP
  • 4 real-life NLP projects:
    • Categorization of sports articles
    • Social media information extraction
    • SMS spam classification
    • Hate speech classification

Possible Career prospects after doing Natural Language Processing Course:

  • Natural Language Processing (NLP) Engineer

  • Data Scientist

  • Chatbot Engineer

Pre-requisites for Natural Language Processing (NLP) using Python Course:

  • This course requires you to know Machine Learning

  • Familiarity with Python would be an advantage (It is taught in the course as well!)

  • No requirement of past experience on NLP

Highlights of Natural Language Processing (NLP) using Python

  • 4 Real Life Projects

  • Live Q & A Session

    Every Thursday 9 pm to 10 pm (IST)

Natural Language Processing (NLP) Using Python Course Curriculum

  • 1
    Course Handouts
    • Course Handouts
  • 2
    Module 1 : Introduction to Natural Language Processing
    • Getting Started
    • Welcome to the Course
    • About the Course FREE PREVIEW
    • Introduction to Natural Language Processing
    • Exercise : Introduction to Natural Language Processing
    • Podcast with NLP Researcher Sebastian Ruder
  • 3
    Module 2 : A Refresher to Python
    • Installation steps for Linux
    • Installation steps for Mac
    • Installation steps for Windows
    • Packages Installation
    • Introduction to Python
    • Variables and Operators
    • Exercise : Variables and Operators
    • Python Lists
    • Exercise : Python Lists
    • Dictionaries
    • Exercise : Dictionaries
    • Conditional Statements
    • Exercise : Conditional Statements
    • Loops
    • Exercise : Loops
    • Functions
    • Python Functions Practice
    • Exercise : Functions
    • Packages
    • Exercise : Packages
    • Files
    • Exercise : Files
  • 4
    Module 3 : Learn to use Regular Expressions
  • 5
    Module 4 : First Step of NLP - Text Processing
    • Welcome to Module
    • Tokenization and Text Normalization
    • Exercise : Tokenization and Text Normalization
    • Exploring Text Data
    • Part of Speech Tagging and Grammar Parsing
    • Exercise : Part of Speech Tagging and Grammar Parsing
    • Implementing Text Pre-processing Using NLTK
    • Exercise : Implementing Text Pre-processing Using NLTK
    • Natural Language Processing Techniques using spaCy
  • 6
    Module 5 : Extracting Named Entities from Test
    • Welcome to Module
    • Understanding Named Entity Recognition FREE PREVIEW
    • Exercise : Understanding Named Entity Recognition
    • Implementing Named Entity Recognition
    • Exercise : Implementing Named Entity Recognition
    • Named Entity Recognition and POS tagging using spaCy
    • POS and NER in Action : Text Data Augmentation
  • 7
    Module 6 : Feature Engineering for Text
    • Introduction to Text Feature Engineering
    • Count Vector, TFIDF Representations of Text
    • Exercise : Introduction to Text Feature Engineering
    • Understanding Vector Representation of Text
    • Exercise : Understanding Vector Representation of Text
    • Understanding Word Embeddings
    • Word Embeddings in Action - Word2Vec
    • Word Embeddings in Action - GloVe
  • 8
    Module 7 : Mastering the Art of Text Cleaning
    • Introduction to Text Cleaning Techniques Part 1
    • Exercise : Introduction to Text Cleaning Techniques Part 1
    • Introduction to Text Cleaning Techniques Part 2
    • Exercise : Introduction to Text Cleaning Techniques Part 2
    • Text Cleaning Implementation
    • Exercise : Text Cleaning Implementation
    • NLP Techniques using spaCy
  • 9
    Module 8 : Project I - Social Media Information Extraction
    • Project I - Social Media Information Extraction
  • 10
    Module 9 : Interpreting Patterns from Text - Topic Modelling
    • Introduction to Topic Modelling
    • Exercise : Introduction to Topic Modelling
    • Understanding LDA
    • Exercise : Understanding LDA
    • Implementation of Topic Modelling
    • Exercise : Implementation of Topic Modelling
    • LSA for Topic Modelling
  • 11
    Project: Categorization of Sports Articles
    • Understanding the Problem Statement
    • Importing Dataset
    • Text Cleaning and Pre-processing
    • Categorizing Articles using Topic Modelling
  • 12
    Module 10.1 : Machine Learning Algorithms
    • Note
    • Types of Machine Learning Algorithms
    • Logistic Regression
    • Decision Tree
    • Naive Bayes
    • SVM (Support Vector Machine)
    • Random Forest
  • 13
    Module 10.2 : Understanding Text Classification
    • Overview of Text Classification
    • Exercise : Overview of Text Classification
  • 14
    Module 11.1 : Introduction to Deep Learning (Optional)
    • Note
    • Getting started with Neural Network
    • Exercise : Getting started with Neural Network
    • Understanding Forward Propogation
    • Exercise : Forward Propogation
    • Math Behind Forward Propagation
    • Exercise : Math Behind Forward Propagation
    • Error and Reason for Error
    • Exercise : Error and Reason for Error
    • Gradient Descent Intuition
    • Understanding Math Behind Gradient Descent
    • Exercise : Gradient Descent
    • Optimizer
    • Exercise : Optimizer
    • Back Propagation
    • Exercise : Back Propagation
    • Why Keras?
    • Exercise : Why Keras?
    • Building a Neural Network for Text Classification
    • Why CNN?
    • Exercise : Why CNN?
    • Understanding the working of CNN Filters
    • Exercise : Understanding the working of CNN Filters
    • Introduction to Padding
    • Exercise : Introduction to Padding
    • Padding Strategies
    • Exercise : Padding Strategies
    • Padding Strategies in Keras
    • Exercise : Padding Strategies in Keras
    • Introduction to Pooling
    • Exercise : Introduction to Pooling
    • CNN architecture and its working
    • Exercise : CNN architecture and its working
    • Introduction to Recurrent Neural Networks
    • What are Recurrent Neural Networks?
    • Understanding a Recurrent Neuron in Detail
    • Forward Propagation in a Recurrent Neuron in Excel
    • Back propagation in a Recurrent Neural Network(BPTT)
    • Vanishing and Exploding Gradient Problem
    • Limitations of RNNs
    • Improvement over RNN: LSTM (Long Short-Term Memory) Networks
    • Architecture of LSTMs
  • 15
    Module 11.2 : Deep Learning for NLP
    • Deep Learning for NLP Part 1
    • Exercise : Deep Learning for NLP Part 1
    • Deep Learning for NLP Part 2
    • Exercise : Deep Learning for NLP Part 2
    • Text Generation Using LSTM
    • Exercise : Text Generation Using LSTM
  • 16
    Module 12 : Project II – SMS Spam Classification
    • Text Cleaning
    • Feature Engineering
    • Advanced Feature Engineering
    • Combining Features
    • ML Classifier
    • Spam Classification using Deep Learning
  • 17
    Module 13 : Project III – Hate Speech Classification
    • Project III
  • 18
    Module 14 : Bonus Section (Advance NLP tools)
    • Text Classification & Word Representations using FastText (An NLP library by Facebook)
    • Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library
    • Introduction to StanfordNLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code)
    • A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text
    • Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
    • 8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP)
    • Geo-coding using NLP by Shantanu Bhattacharyya and Farhat Habib
    • Demystifying the What, Why and How of Chatbot by Sonny Laskar
    • Sentiment Analysis using NLP and Deep Learning by Jeeban Swain
    • Identifying Location using Clustering and Language Model - By Divya Choudhary
    • Building Intelligent Chatbots from Scratch
  • 19
    Module 15 : Where to go from here?
    • Where to go from here?

Project-Social Media Information Extraction

This project is designed to teach you how to extract relevant information such as entities, ngrams, keywords and sentiments from social media data using NLP techniques. The project highlights the importance of nlp techniques to extract business insights from the text data.
Project-Social Media Information Extraction

Project-SMS Spam Classification

This project is about the classification of SMS text messages as spam or nonspam. In this project, the students will learn to preprocess, feature engineering techniques, and text classification techniques using machine learning models and the CNN model.
Project-SMS Spam Classification

Project-Hate Speech Classification

Hate speech is an unfortunately common occurrence on the Internet. Often social media sites like Facebook and Twitter face the problem of identifying and censoring problematic posts while weighing the right to freedom of speech. The importance of detecting and moderating hate speech is evident from the strong connection between hate speech and actual hate crimes. Early identification of users promoting hate speech could enable outreach programs that attempt to prevent an escalation from speech to action. The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
Project-Hate Speech Classification

Project- Categorization of Sports Articles

Document categorization or segregation is an important NLP task which is used across a wide range of industries. In this project, we will learn to segregate sports articles using an unsupervised technique called Topic Modelling. We will categorize the articles based on the content of the articles, i.e., similar articles will be grouped together.
Project- Categorization of Sports Articles


  • Shivam Bansal

    Shivam Bansal

    Shivam Bansal is an experienced full stack data scientist with more than 5 years of experience. He has led the development and execution of multiple end-to-end data science and analytics products for a number of clients from Insurance, Healthcare, Retail, and Academia domain. He has an extensive experience with natural language processing and unstructured data analysis. He is currently ranked 2nd in Kaggle Kernels ranking. He is an author of a book chapter on Deep Learning and has also shared a number of top viewed articles on AnalyticsVidhya.


  • Who should take this course?

    This course is for people who are looking to get into the field of Natural Language Processing, or those who want to brush up their knowledge of NLP and get familiar with the trends in the field. The course provides you everything you need to know to become an NLP practitioner

  • I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?

    The course assumes prior background in Machine Learning. So we would recommend you to be aware of basics of Machine Learning before going through this course.

  • Do I need to install any software before starting the course?

    Yes, you will get information about all installations as part of the course.

  • What is the refund policy?

    The fee for this course is non-refundable.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • Do I get a certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the course.

  • What is the fee for this course?

    Fee for this course is INR 10,999

  • How long I can access the course?

    You will be able to access the course material for six months since the start of the course.

  • When will the classes be held in this course?

    This is an online self-paced course, which you can take any time at your convenience over the 6 months after your purchase.

Support for Natural Language Processing (NLP) using Pyhton Course

Support for Natural Language Processing (NLP) Using Python course can be availed through any of the following channels: