Natural Language Processing:Concept along with Case Study

Free Course: Natural Language Processing (NLP), Text Processing, Machine Learning, Spam Filter [Python]
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.

Course content

3 sections19 lectures1h 31m total length
  • What is Natural Language Processing (NLP)
    04:49
  • Tokenization
    02:15
  • Stop Words Removal
    03:28
  • N-Grams
    03:46
  • Stemming
    01:42
  • Word Sense Disambiguation
    02:04
  • Count Vectorizer
    05:34
  • TF-IDF Vectorizer
    07:30
  • Hashing Vectorizer
    04:21

Instructor

Senior Developer
Rishi Bansal
  • 4.3 Instructor Rating
  • 681 Reviews
  • 38,857 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.