Home>Courses>Building Grammatical Error Correction Models with Python

Intermediate

10h

Certificate of Completion

Building Grammatical Error Correction Models with Python

Learn to build spell checkers and grammar correction models using Python. Explore NLP packages, POS tagging, heuristic methods, and transformer-based spellcheckers for practical use.

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This course includes

37 Lessons

1 Project

12 Playgrounds

11 Challenges

12 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

In this course, you’ll learn the details of spell check and grammatical error correction systems by creating them with their basic building blocks. You’ll explore natural language processing packages like NLTK, pandas, spaCy, Fuzz, GECToR, HuggingFace, and more. You’ll build the Norvig spellchecker and understand how modern machine learning-based spellcheckers work. This is followed by the mathematical concepts required for identifying part-of-speech (POS) tags for grammatical error checking. You’ll then ...Show More

In this course, you’ll learn the details of spell check and grammatical error correction systems by creating them with their bas...Show More

What You'll Learn

An understanding of natural language distance techniques and how these are used in spell check systems

Hands-on experience building spellcheckers from scratch

An understanding of part-of-speech tagging and how it’s utilized in building grammar correction systems

An understanding of neural machine translation as a methodology for building modern grammar correction systems

Hands-on experience building transformer-based grammar correction systems using HuggingFace

An understanding of natural language distance techniques and how these are used in spell check systems

Course Content

Introduction

2 Lessons

Get familiar with grammar parsing, spell checking, and essential NLP terminology in Python.

Course Introduction

Essential Terminology

Edit Distance

7 Lessons

Discover the logic behind edit distance, its algorithms, and practical NLP applications.

What Is Edit Distance?

Calculating Minimum Edit Distance

Implementation of Wagner-Fischer Algorithm

Distance Calculations in NLTK

Distance Calculations in Fuzzy

Summary: Edit Distance

Quiz: Edit Distance

Basic Spellchecker

8 Lessons

Examine spellchecker construction, models like Norvig's, and improve error correction accuracy.

Putting It All Together

Challenge: Improving Our Basic Spellchecker

Quiz: Basic Spellchecker

Modern Spell Check Methods

5 Lessons

Apply your skills to modern spell check methods using SymSpell, machine learning, and transformers.

SymSpell: Symmetric Delete Spelling Correction

Machine Learning-Based Spellchecker

What Is a Transformer?

Transformer-Based Spell Check

Quiz: Modern Spellcheck

Part-of-Speech Tagging

6 Lessons

Map out the steps for POS tagging using HMMs, Viterbi, and NLTK tools.

What Is Part-of-Speech Tagging?

Hidden Markov Models

Part-of-Speech Tagging Using the Viterbi Algorithm

Challenge: Viterbi Forward Pass

Using NLTK Part-of-Speech Tagger

Quiz: Part-of-Speech Tagging

Basic Grammatical Error Checking

5 Lessons

Simplify complex topics in rule-based grammar checking and POS tagging implementation.

Part-of-Speech Tagging and Rule-Based Grammar Checking

Creating Rule-Based Grammar Objects

Challenge: Applying Part-of-Speech Tagging

Building a Basic Grammar Checker

Quiz: Basic Grammar Checker

Modern Grammar Error Correction Methods

3 Lessons

Practice using modern transformer-based methods for effective grammar error correction.

Neural Machine Translation

Grammar Error Correction with Transformer (GECToR)

Quiz: Modern Grammar Correction Methods

Project: Simple Transformer

Project

Conclusion

1 Lessons

Step through the essential techniques and methods for developing grammatical error correction models.

Summary

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Project

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