HomeCoursesText Preprocessing with Python

4.8

Intermediate

16h

Updated 5 months ago

Text Preprocessing with Python

Gain insights into text preprocessing with Python. Explore text cleaning, normalization, and advanced techniques like BoW and TF-IDF. Discover skills to handle unstructured data effectively for NLP.

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Overview

Content

Reviews

This course is designed to empower you with essential skills for effectively handling text data in the context of natural language processing (NLP). You’ll embark on a transformative journey that will equip you with a solid foundation in text manipulation, enabling you to tackle the challenges of unstructured data. The course discusses both fundamental and advanced text preprocessing techniques. You’ll learn how to clean text and remove noise, irrelevant characters, and inconsistencies in text data. Once the data is ready for analysis, you’ll learn text normalization techniques such as stemming, lemmatization, and casing. In addition to mastering preprocessing fundamentals, you’ll also learn techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF). By the end of the course, you’ll be able to position yourself for success in a data-centric world where the ability to extract meaning from unstructured textual information is a prized skill.

This course is designed to empower you with essential skills for effectively handling text data in the context of natural langua...Show More

WHAT YOU'LL LEARN

An understanding of the significance of text preprocessing in natural language processing and its impact on text analysis tasks

Hands-on experience applying various text-cleaning techniques, including the use of regular expressions and lowercase and uppercase transformations

The ability to evaluate the effectiveness of different text normalization techniques

Familiarity with designing and implementing different text representation models, such as bag-of-words and TF-IDF

Hands-on experience applying various text preprocessing techniques and models in a real-world application

An understanding of the significance of text preprocessing in natural language processing and its impact on text analysis tasks

Content

60 Lessons1 Project10 Quizzes26 Challenges

About This Course

1 Lessons

Get familiar with the importance and techniques of text preprocessing for effective NLP.

Course Introduction

Introduction To Text Preprocessing

4 Lessons

Look at text preprocessing techniques, data types, and processing stages for better analysis.

Overview of Text Preprocessing and Its Importance

Types of Text Data and Its Characteristics

The Stages of Text Preprocessing

Quiz: Introduction to Text Preprocessing

Regular Expressions

6 Lessons

Go hands-on with regular expressions to identify patterns, preprocess, and extract text data.

Basics of Regular Expressions

Regular Expressions for Text Preprocessing

Advanced Regular Expressions for Text Preprocessing

Code Challenges on Regular Expressions

Solution Explanations: Regular Expressions

Quiz: Regular Expressions

Irrelevant Text Data

7 Lessons

Break down the steps to effectively manage, clean, and preprocess irrelevant text data.

Types of Irrelevant Text Data

Handling Misspellings

Challenges

Best Practices

Code Challenges on Irrelevant Text Data

Solution Explanations: Irrelevant Text Data

Quiz: Irrelevant Text Data

Basic Text Preprocessing Techniques

6 Lessons

Dig deeper into text preprocessing techniques like lowercasing, punctuation removal, and handling special characters.

Lowercasing and Uppercasing Text

Punctuation Removal

Handling Special Characters

Code Challenges on Basic Text Preprocessing Techniques

Solution Explanations: Basic Preprocessing Techniques

Quiz: Basic Text Preprocessing Techniques

Indexing

6 Lessons

Focus on efficient text processing through various indexing methods and implementing code challenges.

Overview of Indexing in Text Preprocessing

Term-Based and Document-Based Indexing

Inverted and Positional Indexing

Code Challenges on Indexing

Solution Explanations: Indexing

Quiz: Indexing

Text Transformation

6 Lessons

Piece together the parts of text tokenization, normalization, stemming, lemmatization, and transformation challenges.

Text Tokenization

Text Normalization

Stemming and Lemmatization

Code Challenges on Text Transformation

Solution Explanations: Text Transformation

Quiz: Text Transformation

Text Representation

6 Lessons

Learn how to use text representation techniques like BoW, TF-IDF, and word embeddings.

Bag-of-Words

Term Frequency-Inverse Document Frequency

Word Embeddings

Code Challenges on Text Representation

Solution Explanations: Text Representation

Quiz: Text Representation

Text Feature Engineering

6 Lessons

Unpack the core of text feature construction, scaling, date handling, and coding challenges.

Feature Construction

Feature Scaling

Working with Dates

Code Challenges on Text Feature Engineering

Solution Explanations: Text Feature Engineering

Quiz: Text Feature Engineering

10.

Advanced Text Preprocessing

6 Lessons

Go hands-on with part-of-speech tagging, named entity recognition, and text classification.

Part-of-Speech Tagging

Named Entity Recognition

Text Classification

Code Challenges on Advanced Text Preprocessing

Solution Explanations: Advanced Text Preprocessing

Quiz: Advanced Text Preprocessing

11.

N-grams

5 Lessons

Grasp the fundamentals of N-grams, their applications in text classification, and practical code challenges.

Introduction to N-Grams

N-Grams for Text Classification

Code Challenges on N-Grams

Solution Explanations: N-Grams

Quiz: N-Grams

Text Classification of Customer Reviews

Project

12.

Conclusion

1 Lessons

Consolidate your skills in essential text preprocessing techniques for advanced NLP tasks.

Course Summary

Text Classification Using PyTorch

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Valentine Mwangi

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