This Natural Language Processing (NLP) Full Course provides a complete journey from beginner to advanced NLP concepts using Python. You will start by understanding the fundamentals of text processing, tokenization, stemming, lemmatization, and part-of-speech tagging. The course then moves to more advanced topics such as sentiment analysis, named entity recognition (NER), and language modeling.

Hands-on projects demonstrate how to build real-world applications, including chatbots, text summarizers, spam detectors, and recommendation systems. You will also learn modern NLP techniques using transformer-based models like BERT, GPT, and other large language models (LLMs).

Throughout the course, practical Python implementations and libraries such as NLTK, spaCy, Hugging Face Transformers, and scikit-learn are used to ensure learners gain both conceptual understanding and technical skills. The course also covers evaluation metrics, model optimization, and deployment strategies for NLP applications.