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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Text Processing

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.

Text Preprocessing with Python

Splitting text based on whitespace characters

Splitting text into individual characters

Labeling each token with its part of speech

Identifying and labeling specific entities in a text

By dividing a sentence into individual words

By dividing text into individual characters

By labeling each token with its part of speech

By dividing a larger piece of text into smaller, individual sentences

How do we perform sentence tokenization?

To avoid ambiguity between words with different capitalizations

To emphasize the importance of certain words

Why do we use lowercase case normalization?

Why do we use lowercase case normalization?

Expanding contractions into their complete form

Converting text to a specific case format

What does handling contractions involve?

What potential issue should we consider when using stemming?

What potential issue should we consider when using stemming?

Which technique is computationally more intensive: stemming or lemmatization?

Which technique is computationally more intensive: stemming or lemmatization?

Test yourself on what you’ve learned about text transformation.

About This Course

Introduction To Text Preprocessing

Regular Expressions

Irrelevant Text Data

Basic Text Preprocessing Techniques

Indexing

Text Transformation

Text Representation

Text Feature Engineering

Advanced Text Preprocessing

N-grams

Text Classification of Customer Reviews

Conclusion

Text Classification Using PyTorch

Quiz: Text Transformation