NLP, Text Analytics and Sequence Models

In this lesson, we will learn NLP, Text Analytics, and Sequence Models. Text data, tokenization, preprocessing, Bag of Words, TF-IDF, embeddings, sentiment analysis, text classification, RNN, LSTM, and Transformer logic will be explained. ## Lesson objective In this lesson, we will learn NLP, Text Analytics and Sequence Models. In the previous lesson, we covered End-to-End Machine Learning Case Study. We explained business problem definition, ML problem definition, target, prediction period, data grain, data sources, feature engineering, model selection, evaluation, threshold, recommendation, deployment-ready output, and monitoring through a real churn case.