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

They have a positive effect on natural language processing tasks.

They do not affect the performance of text classification algorithms.

They cause confusion and ambiguity in written communication.

They don't hinder the search and retrieval processes.

What's the impact of misspellings on text data?

What’s the impact of misspellings on text data?

Different contexts giving different meanings to the same word

Which example illustrates ambiguity in text data?

Which example illustrates ambiguity in text data?

Which technique do we use to improve the preprocessing pipeline incrementally?

Which technique do we use to improve the preprocessing pipeline incrementally?

What's the potential risk of removing certain words from a sentence without considering their context during preprocessing?

What’s the potential risk of removing certain words from a sentence without considering their context during preprocessing?

To ensure that irrelevant text data is included in the dataset

To keep track of the changes made during the preprocessing pipeline

What's the purpose of documenting and tracking changes during preprocessing?

What’s the purpose of documenting and tracking changes during preprocessing?

Test yourself on what you’ve learned about irrelevant text data.

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: Irrelevant Text Data