Natural Language Processing (NLP) for Beginners Using NLTK
- Development
- Jan 24, 2025

Natural Language Processing (NLP) for Beginners Using NLTK, available at Free, has an average rating of 3.95, with 8 lectures, based on 774 reviews, and has 15765 subscribers.
You will learn about By the end of this course, you will know basic operations performed in NLP and tools made available to us by NLTK package. This course is ideal for individuals who are This course is designed for people interested in learning Natural Language Processing (NLP) from scratch. No prior knowledge of NLP techniques is assumed. It is particularly useful for This course is designed for people interested in learning Natural Language Processing (NLP) from scratch. No prior knowledge of NLP techniques is assumed.
Enroll now: Natural Language Processing (NLP) for Beginners Using NLTK
Summary
Title: Natural Language Processing (NLP) for Beginners Using NLTK
Price: Free
Average Rating: 3.95
Number of Lectures: 8
Number of Published Lectures: 8
Number of Curriculum Items: 8
Number of Published Curriculum Objects: 8
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
Who Should Attend
Target Audiences
In this video series, we will start with in introduction to corpus we have at our disposal through NLTK. Once we download the corpus and learn different tricks to access it, we will move on to very useful feature in NLP called frequency distribution. In this section, we will see how calculate, tabulate and plot ?frequency distribution of words. In the next section, we will start learning NLP specific techniques that include:
1. Stemming
2. Lemmatization
3. Tokenization
Course Curriculum
Chapter 1: Using NLTK Corpora
Lecture 1: Introduction to NLTK Corpora
Lecture 2: How to download corpora?
Lecture 3: How to access a corpus in NLTK corpora?
Chapter 2: Working with Frequency Distribution
Lecture 1: Basics of Frequency Distribution
Lecture 2: Conditional Frequency Distribution
Chapter 3: Basic Operations in NLP
Lecture 1: Stemming
Lecture 2: Lemmatization
Lecture 3: Tokenization
Instructors

Harshal Samant
Engineer and a Machine Learning Enthusiast
Rating Distribution
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
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