Technical requirements
To effectively read and understand this chapter, it is essential to have a solid foundation in various technical areas. A strong grasp of fundamental concepts in NLP, ML, and linear algebra is crucial. Familiarity with text preprocessing techniques, such as tokenization, stop word removal, and stemming or lemmatization, is necessary to comprehend the data preparation stage.
Additionally, understanding basic ML algorithms, such as logistic regression and support vector machines (SVMs), is crucial for implementing text classification models. Finally, being comfortable with evaluation metrics such as accuracy, precision, recall, and F1 score, along with concepts such as overfitting, underfitting, and hyperparameter tuning, will enable a deeper appreciation of the challenges and best practices in text classification.
