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Explore data pipelines in this third installment of the introduction to text analytics with R video. Dive into textual data exploration for pre-processing challenges, utilize the quanteda package for text analytics, and create a prototypical text analytics pre-processing pipeline. Learn about tokenization, lower casing, stop word removal, and stemming. Develop skills to create a document-frequency matrix used for training machine learning models. Access the Kaggle dataset and R code used in the series to practice hands-on. Gain valuable insights into text analytics techniques and their application in data science projects.
