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This course path introduces the core steps used to prepare text for natural language processing workflows. You will work with Python to inspect text datasets, identify basic patterns, and transform raw text into a cleaner format for analysis. The path covers common preprocessing techniques such as lowercasing, tokenization, stopword removal, and stemming. You will then learn how to convert text into numerical features using TF-IDF so that machine learning models can use it. You will apply these skills to text classification tasks using algorithms such as Naive Bayes and logistic regression. By the end, you will understand how to train, test, and evaluate models that classify text into categories.
