Course Summary

Key takeaways

Text preprocessing refers to tasks and techniques we perform on raw text data before further analysis. These techniques are critical for organizations looking to uncover insights. A few examples of text preprocessing techniques include lowercasing, removing special characters and stopwords, and performing tokenization, stemming, lemmatization, and part-of-speech tagging.

The text preprocessing stages

We can look at text preprocessing as a process with many techniques rather than a single action. This process progresses from one stage or step to another until it ends, and it varies from organization to organization and even project to project.

Text preprocessing is crucial for data science and machine learning. In data science, we use text preprocessing techniques for data cleaning and preprocessing, which involves removing irrelevant information from text data and transforming it into a more structured format that can be used for analysis. On the other hand, in machine learning, we use text preprocessing techniques to create datasets for training machine-learning ...