Named Entity Recognition (NER) and Text Classification in NLP focuses on identifying key entities in text and categorizing documents into predefined classes. It helps organizations structure unstructured data and extract meaningful insights from large text datasets. This training explains core concepts such as entity recognition, tokenization, part-of-speech tagging, and contextual text understanding. It also covers text classification methods, feature extraction, machine learning models, and deep learning techniques using modern NLP frameworks. You will learn how NER extracts names, locations, and organizations from text. You will also learn how text classification supports spam detection, sentiment analysis, and content tagging. The course highlights best practices for building accurate, scalable, and production-ready NLP systems.
