NLTK: Build Document Classifier & Spell Checker with Python

NLTK: Build Document Classifier & Spell Checker with Python, available at $44.99, has an average rating of 3.65, with 48 lectures, 5 quizzes, based on 164 reviews, and has 863 subscribers.

You will learn about NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot Text Tokenization Text Normalization: Stemming & Lemmatization Text Tagging: Unigram, N-Gram, Regex Text Classification Project 1: Gender Prediction Application Project 2: Document Classification Application Information Extraction from Text: Chunking, Chinking, Name Entity Recognition Source Code *.py Files of All Lectures English Captions for All Lectures Q&A board to send your questions and get them answered quickly This course is ideal for individuals who are This Natural Language Processing (NLP) tutorial is desined for Python programmers who want to learn more about Natural Language Toolkit (NLTK). It is particularly useful for This Natural Language Processing (NLP) tutorial is desined for Python programmers who want to learn more about Natural Language Toolkit (NLTK).

Enroll now: NLTK: Build Document Classifier & Spell Checker with Python

Summary

Title: NLTK: Build Document Classifier & Spell Checker with Python

Price: $44.99

Average Rating: 3.65

Number of Lectures: 48

Number of Quizzes: 5

Number of Published Lectures: 46

Number of Published Quizzes: 4

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 50

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot
Text Tokenization
Text Normalization: Stemming & Lemmatization
Text Tagging: Unigram, N-Gram, Regex
Text Classification
Project 1: Gender Prediction Application
Project 2: Document Classification Application
Information Extraction from Text: Chunking, Chinking, Name Entity Recognition
Source Code *.py Files of All Lectures
English Captions for All Lectures
Q&A board to send your questions and get them answered quickly

Who Should Attend

This Natural Language Processing (NLP) tutorial is desined for Python programmers who want to learn more about Natural Language Toolkit (NLTK).

Target Audiences

This Natural Language Processing (NLP) tutorial is desined for Python programmers who want to learn more about Natural Language Toolkit (NLTK).

This Natural Language Processing (NLP) tutorial covers core basics of NLP using the well-known Python package Natural Language Toolkit (NLTK). The course helps trainees become familiar with common concepts like tokens, tokenization, stemming, lemmatization, and using regex for tokenization or for stemming. It discusses classification, tagging, normalization of our input or raw text. It also covers some machine learning algorithms such as Naive Bayes.

After taking this course, you will be familiar with the basic terminologies and concepts of Natural Language Processing (NLP) and you should be able to develop NLP applications using the knowledge you gained in this course.

What is Natural Language Processing (NLP)?

Natural language processing, or NLP for short, is the ability of a computer program to understand, manipulate, analyze, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation, and spam detection.

What is NLTK?

The Natural Language Toolkit (NLTK) is a suite of program modules and data-sets for text analysis, covering symbolic and statistical Natural Language Processing (NLP). NLTK is written in Python. Over the past few years, NLTK has become popular in teaching and research.

NLTK includes capabilities for tokenizing, parsing, and identifying named entities as well as many more features.

This Natural Language Processing (NLP) tutorial mainly cover NLTK modules.

About the course

This Natural Language Processing (NLP) tutorial is basically designed to make you understand the fundamental concepts of Natural Language Processing (NLP) with Python, and we will be learning some machine learning algorithms as well because natural language processing and machine learning move hand in hand as NLP employs machine learning techniques to learn and understand what a sentence is saying, or what a user has said and it sends an appropriate response back.�

So, by the end of this course, I hope you will have a clear idea, a clear view of the core fundamental concepts of NLP and how we can actually make applications using these core concepts.

Looking forward to seeing you in the course.

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Keywords: Natural Language Processing (NLP) tutorial; Python NLTK; Machine Learning; Sentiment Analysis; Data Mining; Text Analysis; Text Processing

Course Curriculum

Chapter 1: Getting Started with NLTK (Natural Language Processing Toolkit)

Lecture 1: Introduction to NLP

Lecture 2: Course Technical Requirements

Lecture 3: Installing and Setting Up NLTK

Lecture 4: NLTK Accessing Texts

Lecture 5: Basic Functions: concordance, similar, dispersion_plot, count

Lecture 6: Summary: NLTK Basic Functions

Lecture 7: Frequency Distribution with NLTK

Lecture 8: Frequency Distribution on Your Text with NLTK

Chapter 2: Do you want to learn a specific NLP topic?

Lecture 1: Do you want to learn a specific NLTK or NLP topic?

Chapter 3: Corpora

Lecture 1: Accessing Corpora

Lecture 2: Loading Your Own Corpus

Lecture 3: Conditional Frequency Distribution

Lecture 4: Lexical Resources Vocabulary

Lecture 5: Terminology

Chapter 4: Processing Raw Text with NLTK

Lecture 1: NLP Pipeline

Lecture 2: Tokenization

Lecture 3: Regular Expressions

Lecture 4: Applications of Regex

Lecture 5: Stemming

Lecture 6: Lemmatization

Lecture 7: Regex for Tokenization

Chapter 5: Categorizing and Tagging Words with NLTK

Lecture 1: Tagger

Lecture 2: Tagged Corpus

Lecture 3: The Default Tagger

Lecture 4: Regexp Tagger

Lecture 5: Unigram Tagger

Lecture 6: Ngram Tagger

Chapter 6: Sentiment Analysis: Text Classification Practical Projects

Lecture 1: Machine Learning Overview

Lecture 2: Logic Of Naive Bayes

Lecture 3: Project #1: Gender Prediction Application – Part 1

Lecture 4: Project #1: Gender Prediction Application – Part 2

Lecture 5: Project #1: Gender Prediction Application – Part 3

Lecture 6: Project #2: Document Classifier Application

Chapter 7: Extracting Info from Text

Lecture 1: Information Extraction Architecture

Lecture 2: Chunking Overiew

Lecture 3: Chunking in Coding

Lecture 4: Exercise: Named Entity Recognition

Lecture 5: Chinking

Lecture 6: Stanford NLP API

Chapter 8: NLP Course Concolusion

Lecture 1: Conclusion

Chapter 9: Advanced NLTK Topics

Lecture 1: Edit Distance Example

Lecture 2: Edit Distance – Spelling Checker

Lecture 3: Appendix: List of Correct Words for Spelling Checkers

Lecture 4: Edit Distance – Plagiarism Checker / Translation Memory

Chapter 10: Bonus Material

Lecture 1: More NLP Tutorials

Lecture 2: What's Next for You?

Instructors

GoTrained Academy
eLearning Professionals
Waqar Ahmed

Rating Distribution

1 stars: 6 votes
2 stars: 18 votes
3 stars: 38 votes
4 stars: 54 votes
5 stars: 48 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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