Natural Language Processing:Concept along with Case Study

Free Course: Natural Language Processing (NLP), Text Processing, Machine Learning, Spam Filter [Python]
Free tutorial
Rating: 3.8 out of 5 (101 ratings)
5,624 students
1hr 32min of on-demand video
English
English [Auto]

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
Rishi Bansal
  • 4.1 Instructor Rating
  • 848 Reviews
  • 44,915 Students
  • 6 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.

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