Summary

In this chapter, we embarked on a comprehensive exploration of text classification, an indispensable aspect of NLP and ML. We delved into various types of text classification tasks, each presenting unique challenges and opportunities. This foundational understanding sets the stage for effectively tackling a broad range of applications, from sentiment analysis to spam detection.

We walked through the role of N-grams in capturing local context and word sequences within text, thereby enhancing the feature set used for classification tasks. We also illuminated the power of the TF-IDF method, the role of Word2Vec in text classification, and popular architectures such as CBOW and skip-gram, giving you a deep understanding of their mechanics.

Then, we introduced topic modeling and examined how popular algorithms such as LDA can be applied to text classification.

Lastly, we introduced a professional paradigm for leading an NLP-ML project in a business or research setting....