Introduction to Natural Language Processing (NLP)
What you'll learn
- Work with text data using the Natural Language Tool Kit.
- Load and manipulate custom text data.
- Analyze text to discover, sentiment, important key words, and statistics.
Course content
Course Introduction1 lecture • 4min
- Course Intro and OutlinePreview03:38
Setup2 lectures • 9min
Python Refresher4 lectures • 13min
NLTK and the Basics9 lectures • 47min
Tokenization , Tagging, Chunking8 lectures • 37min
Custom Sources8 lectures • 21min
Projects6 lectures • 50min
Appendix3 lectures • 1min
Requirements
- A computer running Windows, OS X, or Linux.
- Basic Python programming knowledge.
Description
This course introduces Natural Language Processing through the use of python and the Natural Language Tool Kit. Through a practical approach, you'll get hands on experience working with and analyzing text.
As a student of this course, you'll get updates for free, which include lecture revisions, new code examples, and new data projects.
By the end of this course you will:
- Have an understanding of how to use the Natural Language Tool Kit.
- Be able to load and manipulate your own text data.
- Know how to formulate solutions to text based problems.
- Know when it is appropriate to apply solutions such as sentiment analysis and classification techniques.
Who this course is for:
- This course is for anyone who is not familiar with Natural Language Processing and is looking for a way to start.
- This course is probably not for you if you already have an understanding of Natural Language Processing and the Natural Language Tool Kit.
Instructor
![Brian Sacash](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.2 Instructor Rating
- 1,868 Reviews
- 7,456 Students
- 1 Course
Brian has a BS in physics and MS in Quantitative Analysis from the University of Cincinnati with over 8 years experience in data analysis. Over the course of his career he has developed a skill set in natural language processing analysis and big data. He has helped solved data problems from the Department of Defense to global financial institutions. He previously was a consultant where he helped organizations understand their data through advanced analytics methods. Brian currently works on developing methods and software for large data systems.