Natural Language Processing:Concept along with Case Study

Natural Language Processing:Concept along with Case Study, available at Free, has an average rating of 4.35, with 19 lectures, based on 142 reviews, and has 7470 subscribers.

You will learn about What are various text processing techniques and their implementation in python. Case Study: Role of Hashing in Spam Filter compared to Countvectorizer. This course is ideal for individuals who are People willing to learn NLP and looking forward to build career in Machine Learning. It is particularly useful for People willing to learn NLP and looking forward to build career in Machine Learning.

Enroll now: Natural Language Processing:Concept along with Case Study

Summary

Title: Natural Language Processing:Concept along with Case Study

Price: Free

Average Rating: 4.35

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

What are various text processing techniques and their implementation in python.
Case Study: Role of Hashing in Spam Filter compared to Countvectorizer.

Who Should Attend

People willing to learn NLP and looking forward to build career in Machine Learning.

Target Audiences

People willing to learn NLP and looking forward to build career in Machine Learning.

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.

Course Curriculum

Chapter 1: Introduction to Natural Language Processing

Lecture 1: What is Natural Language Processing (NLP)

Lecture 2: Tokenization

Lecture 3: Stop Words Removal

Lecture 4: N-Grams

Lecture 5: Stemming

Lecture 6: Word Sense Disambiguation

Lecture 7: Count Vectorizer

Lecture 8: TF-IDF Vectorizer

Lecture 9: Hashing Vectorizer

Chapter 2: Text Preprocessing – Python Code

Lecture 1: Tokenization – Python

Lecture 2: Stop Word Removal – Python

Lecture 3: N-Grams – Python

Lecture 4: Stemming – Python

Lecture 5: Word Sense Disambiguation – Python

Lecture 6: Count Vectorizer – Python

Lecture 7: TF-IDF Vectorizer – Python

Lecture 8: Hashing Vectorizer – Python

Chapter 3: Case Study: Spam Filter

Lecture 1: Spam Filter using CountVectorizer

Lecture 2: Spam Filter using Hashing

Instructors

Rishi Bansal
Senior Developer

Rating Distribution

1 stars: 3 votes
2 stars: 5 votes
3 stars: 40 votes
4 stars: 47 votes
5 stars: 47 votes

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