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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.


Key Takeways from the Natural Language Processing using Python course:

  • Understand the nature of text data and how to work with it.
  • Learn about different text pre-processing techniques.
  • Learn how to perform Parts-of-Speech Tagging and Named Entity Recognition
  • Learn about the important techniques for feature extraction from text.
  • Understand how deep learning can be used to solve complex tasks in NLP.
  • Implement awesome NLP projects using Deep Learning.


Why Natural Language Processing?


Highlights of Natural Language Processing (NLP) using Python

  • 7 Real Life Projects

  • Live Q & A Session

    Interact with experts on live chat for 1 hour daily.

Natural Language Processing (NLP) Using Python Course Curriculum

  • 1
    Welcome to the course
    • DataHack Summit 2019 - India’s largest Applied Artificial Intelligence and Machine Learning Conference
  • 2
    Course Handouts
    • Download Course Handouts
  • 3
    Module 1 : Introduction to Natural Language Processing
    • Getting Started
    • Knowing each other
    • 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
  • 4
    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
  • 5
    Module 3 : Learn to use Regular Expressions
  • 6
    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
  • 7
    Module 5 : Extracting Named Entities from Text
    • 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
    • Assignment: Share your learning and build your profile
  • 8
    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
  • 9
    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
  • 10
    Module 8 : Project I - Social Media Information Extraction
    • Project I - Social Media Information Extraction
  • 11
    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
  • 12
    Module 10: Project II - Categorization of Sports Articles
    • Understanding the Problem Statement
    • Importing Dataset
    • Text Cleaning and Pre-processing
    • Categorizing Articles using Topic Modelling
  • 13
    Module 11.1 : Machine Learning Algorithms
    • Note
    • Types of Machine Learning Algorithms
    • Logistic Regression
    • Decision Tree
    • Naive Bayes
    • SVM (Support Vector Machine)
    • Random Forest
  • 14
    Module 11.2 : Understanding Text Classification
    • Overview of Text Classification
    • Exercise : Overview of Text Classification
    • Assignment: Share your learning and build your profile
  • 15
    Module 12.1 : 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
  • 16
    Module 12.2 : 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
  • 17
    Module 13 : Project III – SMS Spam Classification
    • Dataset download
    • Text Cleaning
    • Feature Engineering
    • Advanced Feature Engineering
    • Combining Features
    • ML Classifier
    • Spam Classification using Deep Learning
  • 18
    Module 14 : Project IV – Hate Speech Classification
    • Project III
  • 19
    Module 15 : Project V – Building Auto-Tagging System
    • Overview of Auto-Tagging System
    • Introduction to Dataset and Performance Metrics
    • Auto-Tagging Implementation Using Machine Learning Part-1
    • Auto-Tagging Implementation Using Machine Learning Part-2
    • Auto-Tagging Implementation Using Deep Learning
  • 20
    Module 16 : Recurrent Neural Networks
    • Why RNN
    • Introduction to RNN: Shortcomings of an MLP
    • Introduction to RNN: RNN Architecture
    • Training an RNN: Forward propagation
    • Training an RNN: Backpropagation through time
    • Need for LSTM/GRU
    • Long Short Term Memory (LSTM)
    • Gated Recurrent Unit (GRU)
    • Project: Categorisation of websites using LSTM and GRU I
    • Dataset and Notebook
    • Project: Categorisation of websites using LSTM and GRU II
  • 21
    Module 17 : Introduction to Language Modeling in NLP
    • Overview : Language Modeling
    • What is a Language Model in NLP?
    • N-gram Language Model
    • Implementing an N-gram Language Model - I
    • Implementing an N-gram Language Model - II
    • Neural Language Model
    • Implementing a Neural Language Model
  • 22
    Module 18 : Sequence-to-Sequence Modeling
    • Intuition Behind Sequence-to-Sequence Modeling
    • Need for Sequence-to-Sequence Modeling
    • Understanding the Architecture of Sequence-to-Sequence
    • Understanding Functioning of Encoder and Decoder
    • Case Study: Building an Spanish to English Machine Translation Model
    • Preprocessing of Text Data
    • Converting Text to Integer Sequences
    • Model Building and Inference
  • 23
    Module 19 : Project VI - Summarization of Customer Reviews
    • Introduction
    • Preprocessing and Feature Creation
    • Model Building and Summary Generation
  • 24
    Module 20 : Project VII - Build your first Chatbot
    • Introduction
    • About this module
    • Overview of Conversational Agents
    • Project - Foodbot
    • Overview of Rasa Framework
    • System Setup
    • Rasa NLU: Understanding user intent from a message
    • Rasa NLU: Extracting intents from a user's message
    • Rasa Core: Making your chatbot conversational
    • Working with Zomato API
    • Create a Workspace in Slack
    • Deploying to Slack
    • Assignment: Share your learning and build your profile
  • 25
    Module 21 : Bonus Section (Advance NLP tools)
    • Getting started with Bonus Section
    • 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
  • 26
    Module 22 : Where to go from here?
    • Where to go from here?

Project-Social Media Information Extraction (In-class)

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 (In-class)

Project-SMS Spam Classification (In-class)

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 (In-class)

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 (In-class)

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 (In-class)

Project - Building Auto Tagging System (In-Class)

Automatic tagging of questions on platforms like stackoverflow is quite vital to build a healthy user engagement at the platform. These tags help both, the users seeking solutions to their problems and the experts capable of solving those problems, find the relevant questions easily. In this project, we will build an automatic tagger for the Stack Overflow questions.
Project - Building Auto Tagging System (In-Class)

Project- Build your first Chatbot (In-Class)

Chatbots are everywhere today, from booking your flight tickets to ordering food, chances are that you have already interacted with one. In this module, you will build your first chatbot to search for restaurants online and learn how to use it in a real-world application by deploying it on Slack.
Project- Build your first Chatbot (In-Class)

Project- Summarization of Customer Reviews (In-class)

Automatic Text Summarization is a process of generating a concise and meaningful summary of text from multiple text resources such as news articles, blog posts, research papers, customer reviews, emails, and tweets. In this project, we will create short summaries of customer reviews on the women's clothing dataset, using sequence-to-sequence modeling.
Project- Summarization of Customer Reviews (In-class)

Instructor

  • 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.
  • Prateek Joshi

    Prateek Joshi

    Prateek is a Data Scientist at Analytics Vidhya. He has a multidisciplinary academic background and rich experience in BFSI and E-Learning industries. Prateek's strengths include expertise in Natural Language Processing (NLP) and Machine Learning. He is well versed in Python, R and most of the libraries and frameworks around machine learning and NLP. He has taken various trainings around NLP and Data Science and he is also a course instructor and content creator at Analytics Vidhya.
  • Mohd Sanad  Zaki Rizvi

    Mohd Sanad Zaki Rizvi

    Mohd Sanad Zaki Rizvi is a Data Scientist at Analytics Vidhya. Sanad’s strength includes his expertise in Machine Learning, NLP and Software Engineering. He has conducted multiple trainings around Data Science and NLP and will be your instructor for this course. He has previously worked in a research capacity at the University of Southern Califonia, Los Angeles where he was working at the intersection of NLP and Deep Learning to build better virtual STEM mentors. When Sanad is not busy trying to explore the breakthroughs in NLP, he is an avid contributor to open-source projects including the Python programming language.

Here's what our students have to say about our Natural Language Processing (NLP) using Python course

  • Good job of covering very complex subject matter.

    Carl Ware

    Very comprehensive, challenging, and worthwhile

    Very comprehensive, challenging, and worthwhile

    Read Less
  • A very helpful course

    Bianca Aguglia

    The course had a lot of information (which made it overwhelming at times) but it was presented very well. I liked all the practical examples and exercises. T...

    Read More

    The course had a lot of information (which made it overwhelming at times) but it was presented very well. I liked all the practical examples and exercises. Thank you for a great learning experience.

    Read Less
  • Easy to understand course with good examples

    Umang Verma

    I took this course soon after its launch as I was working on a few NLP projects. The course content is easy to understand and it has good projects. Initially...

    Read More

    I took this course soon after its launch as I was working on a few NLP projects. The course content is easy to understand and it has good projects. Initially it felt that there were not enough projects - but the team added them to make it a perfect course for any aspirant in NLP.

    Read Less

FAQ

  • 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.

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