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Price: INR 8,799 (10,999)
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About This Course
There is a surplus of data all around us, but according to industry estimates, only 21% of the available data is present in structured form where information is directly accessible without processing. Organizations today deal with huge amount and wide variety of data – calls from customers, their emails, tweets, data from mobile applications and what not. It takes a lot of effort and time to make this data useful. One of the core skills in extracting information from text data is Natural Language Processing (NLP). .
Natural Language Processing (NLP) is the art and science which helps us extract information from text and use it in our computations and algorithms. NLP can power many applications, such as language translation, question answering systems, chatbots and document summarizers. Given the increase in content on internet and social media, it is one of the must have skill for all data scientists out there.
This course is designed for people who are looking to get into the field of Natural Language Processing. It provides you everything you need to know to become an NLP practitioner.
As this is a more practical and advanced course, it is required that you have a good grasp on the basics of Machine Learning. Also, familiarity of Python language is an added benifit (although this will be taught in the course)
Introduction to Natural Language Processing
In this module, you will come across the wonderful field of Natural Language Processing(NLP). Here you will get a high level overview of NLP, and the tasks associated with it.
Refresher to Python
For a Data Scientist, having the knowledge of the right tool is very important, which can help him or her to convert ideas to practical working models. This module is a refresher for Python, an industry grade tool for doing NLP tasks.
Learn to use Regular Expressions
The module covers the concept of Regular Expresssions and how they can be used to extract useful information from text.
First Step of NLP - Text Processing
Text is the most unstructured form of all the available data. This module is all about getting the text ready for data analysis. You will get to know techniques such as lemmatizing, stemming, parts of speech tagging and dependency parsing.
Extracting Named Entities from Text
In this module, you will learn about detecting named entities from text, which designate the most useful information from textual data, and how the technique of Named Entity Recognition is implemented using NLTK library in Python.
Interpreting Patterns from Text - Topic Modelling
In this module, you will learn about topic modelling, a technique used for finding hidden patterns among the words in a text.
Mastering the Art of Text Cleaning
Raw text has to be cleaned, before we do predictive modeling on textual data. This module shows you the best practices of cleaning noisy text.
Project I - Social Media Information Extraction
In this project, you will get to apply the learnings of the previous modules to clean tweet data, and analyze it.
Feature Engineering for Text
To analyse a processed textual data, it needs to be converted into features. Depending upon the usage, text features can be constructed using assorted techniques – Syntactical Parsing, Entities / N-grams / word-based features, Statistical features, and word embeddings. You will get to know them in this module.
Understanding Text Classification
This module talks about a specific problem in the field of natural language processing, called text classification. Text classification is widely used in solving real life problems such as Email Spam Identification, Topic Classification of news and Sentiment Analysis.
Deep Learning for NLP
Now you are familiar with the field of NLP, it is crucial for you to be aware of the state-of-the-art techniques used to solve NLP tasks. You will get to know the basics of deep learning techniques, and how deep learning techniques can be leveraged to push the boundaries of what a machine can do to solve NLP tasks.
Project II – SMS Spam Classification
In this project, you will practice how NLP techniques can be applied to solve a real life problem of Spam Detection in SMS, where you have to classify SMS text message as spam or non-spam.
Project III – Hate Speech Classification
This is the final project of the course, where you will get a chance to test your knowledge of the techniques covered in the course. Here your task is to classify racist and sexist tweets from other tweets.
Project1 : 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.
Project2 : 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.
Project3 : 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.
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.
Frequently Asked Questions
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.