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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