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

To store, retrieve, and manipulate data efficiently

Why do we perform indexing on text data?

Which indexing technique involves associating each term with a list of document identifiers?

Which indexing technique involves associating each term with a list of document identifiers?

Document-based indexing focuses on individual words, while term-based indexing focuses on entire documents.

Document-based indexing indexes documents as a whole based on content or metadata, while term-based indexing indexes individual terms.

Document-based indexing preprocesses keywords, while term-based indexing preprocesses document content.

Document-based indexing is memory-intensive, while term-based indexing is computationally intensive.

How does document-based indexing differ from term-based indexing?

How does document-based indexing differ from term-based indexing?

Which text preprocessing technique is _not_ typically associated with term-based indexing?

Which text preprocessing technique is not typically associated with term-based indexing?

It alphabetically organizes terms in documents.

It records the occurrences of terms within documents.

It provides comprehensive document summaries.

How does positional indexing enhance the precision of information retrieval?

How does positional indexing enhance the precision of information retrieval?

Test yourself on what you’ve learned about indexing.

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: Indexing