Natural Language Processing:Concept along with Case Study
Free Course: Natural Language Processing (NLP), Text Processing, Machine Learning, Spam Filter [Python]
Created by Rishi Bansal
What are various text processing techniques and their implementation in python.
Case Study: Role of Hashing in Spam Filter compared to Countvectorizer.
Requirements
- Basic Understanding of Python
- One Laptop with Python IDE installed
- Understanding of Machine learning will be helpful in Case Study however not mandatory
Description
This course provides a basic understanding of NLP. Anyone can opt for this course. No prior understanding of NLP is required. Text Processing like Tokenization, Stop Words Removal, Stemming, different types of Vectorizers, WSD, etc are explained in detail with python code. Also difference between CountVectorizer and Hashing in Spam Filter.
Who this course is for:
- People willing to learn NLP and looking forward to build career in Machine Learning.
Instructor
Senior Developer
- 4.0 Instructor Rating
- 935 Reviews
- 50,609 Students
- 4 Courses
A total of 13 years of experience. I started my career as a programmer. Apart from programming, I have worked on Cloud & Virtualization technology, DevOps and Machine Learning. Also, I have very good knowledge of software design methodologies, information systems architecture, object oriented design, and software design patterns. Teaching is my passion. I hope you will enjoy my course.