Natural Language Processing is the art of extracting information from unstructured text. Learn basics of Natural Language Processing, Regular Expressions & text sentiment analysis using machine learning in this course.
Natural Language Processing (NLP) is basically how you can teach machines to understand human languages and extract meaning from text.
Language as a structured medium of communication is what separates us human beings from animals. We are surrounded by text data all the time sourced from books, emails, blogs, social media posts, news and more.
Natural Language Processing is expected to be worth 30 Billion USD by 2024 with the past few years seeing immense improvements in terms of how well it is solving industry problems at scale.
Natural Language Processing (NLP) today powers many key real-life industry applications, such as:
- Language Translation
- Dialog Systems / Chatbots
- Sentiment Analysis
- Text Summarizers
- Speech Recognition
- Welcome to the Course
- About the Course
- Introduction to Natural Language Processing
- Exercise : Introduction to Natural Language Processing
- Python for Data Science (Optional)
- Welcome to Module
- Understanding Regular Expression
- Implementing Regular Expression in Python
- Exercise : Implementing Regular Expression in Python
- 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
- Build a Basic ML Model for Text Classification
- Where to go from here?
This free course helps you take that first step in the world of Natural Langauge Processing with the following curated topics to help you get started.
- Reading and working with text data using Python In this section you will learn how to import text data using python for further processing
- Learn to use Regular Expressions to extract patterns from text Have you ever thought of how specific patterns such as email IDs are extracted from a long text. Regular Expressions or regex is the Python module that helps you manipulate text data and extract patterns.
- Text preprocessing Text is essentially strings and in order for a machine to work with, it needs to be transformed to numbers which the machine can understand. Also, there are words that you want to clean up from a text such as commonly occuring stopwords such as a, the, has, would and so on
- NLP Project on Sentiment Analysis In this module, you will solve a Sentiment Analysis Project to detect hate speech from text using Machine Learning.
Is it a good time to pursue Natural Language Processing?
A good time to start with NLP is now! With plethora of applications in several markets & industries, NLP has become a highly sought after skill all over the world.
What is the best language to learn for solving NLP tasks?
Natural Language Processing with Python is the way to go and it has been the most popular language in both industry and Academia. Python provides excellent ready made libraries such as NLTK, Spacy, CoreNLP, Gensim, Scikit-Learn & TextBlob which have excellent easy to use functions to work with text data. Deep Learning frameworks like PyTorch, Tensorflow and Keras which are all part of Python Ecosystem are the default choices for using Deep Learning in Natural Langauge Processing.
Do I need to have a PhD to build a career in NLP?
Applied NLP is something that can be mastered by someone with good knowledge of Python and a background in Engineering or quantitative field is a good to have but not a necessity. Natural Language Processing with Python is something that can be taught and learnt with dedicated effort and a good learning path.
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.
Pros: The course is crisp, very informative and a good step to make you understand the type of work involved in NLP Cons: Stemming and Lemmitization coul...Read More
Pros: The course is crisp, very informative and a good step to make you understand the type of work involved in NLP Cons: Stemming and Lemmitization could have been used in final project so that it will then cover all topics taught in courseRead Less
Thanks to the tutorial I've learn't the basics of NLP. I hope it will help me to learn more about natural language processing
Thanks to the tutorial I've learn't the basics of NLP. I hope it will help me to learn more about natural language processingRead Less
Helps Beginners to gain some intuition about NLP, Thanks
Helps Beginners to gain some intuition about NLP, ThanksRead Less
I found this tutorial very useful and interesting. All the basics are covered in this course with examples.
I found this tutorial very useful and interesting. All the basics are covered in this course with examples.Read Less
Great introduction to NLP concepts, it is a bit short even for an introductory course.
Great introduction to NLP concepts, it is a bit short even for an introductory course.Read Less
Good intro to the topic
Good intro to the topicRead Less
Good course content and meaningful tutorials
Good course content and meaningful tutorialsRead Less
Who should take this course?
This course is for people who want to start their journey in working with text data and take their first step for understanding Natural Language Processing in an applied manner
I have an experience of 2+ years and have no prior knowledge on NLP. Is the course right for me?
The course assumes familiarity with Python and requires understanding of the basics of Machine Learning. No prior knowledge in Natural Language Processing is required.
If I do not meet the requirements to enroll, what should I do?
You can opt for our Free Course Python for Data science in order to get familiarised with Python
What is the fee for this course?
This course is free of cost.
How long would I have access to “Introduction to Natural Language Processing” course?
Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
How much effort will this course take?
You can complete "Introduction to Natural Language Processing" course in a few hours. You are expected to work on the in course project and you can also participate and compete against other participants at Datahack.The time taken in projects varies from person to person.
How can I apply and test my learnings about Natural Language Processing?
You can start by doing the tests at the end of each lesson. In addition, you can apply Natural Language Processing to solve the following Practice Problems at Datahack:
Can I download videos from this course?
We regularly update "Introduction to Natural Language Processing" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.
Which programming language is used to teach Natural Language Processing in this course?
This course uses Python programming language and its open source libraries NLTK and scikit-learn to solve the project