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
- 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
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
- Course Handouts
- 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
- Welcome to Module
- Understanding Regular Expression FREE PREVIEW
- Implementing Regular Expression in Python
- Exercise : Implementing Regular Expression in Python
- Regular Expressions in Action
- 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
- 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
- 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
- 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
- Project I - Social Media Information Extraction
- 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
- Understanding the Problem Statement
- Importing Dataset
- Text Cleaning and Pre-processing
- Categorizing Articles using Topic Modelling
- Types of Machine Learning Algorithms
- Logistic Regression
- Decision Tree
- Naive Bayes
- SVM (Support Vector Machine)
- Random Forest
- Overview of Text Classification
- Exercise : Overview of Text Classification
- 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
- 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
- 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
- Text Cleaning
- Feature Engineering
- Advanced Feature Engineering
- Combining Features
- ML Classifier
- Spam Classification using Deep Learning
- Project III
- 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
- 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
- Where to go from here?
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.
Very comprehensive, challenging, and worthwhile
Very comprehensive, challenging, and worthwhileRead Less
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
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
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 Python course can be availed through any of the following channels: