This course provides a comprehensive introduction to Natural Language Processing (NLP), a field that enables computers to understand, interpret, and generate human language. It begins with an overview of NLP, including its key tasks, challenges, and real-world applications.

You will learn essential text preprocessing techniques such as tokenization, stopword removal, stemming, lemmatization, and data cleaning. The course also introduces fundamental linguistic concepts including morphology, syntax, and semantics, as well as practical techniques like part-of-speech tagging and parsing.

The course explores different methods of text representation, including Bag of Words (BoW), N-grams, and TF-IDF, along with modern word embedding techniques such as Word2Vec, GloVe, and contextual embeddings. You will also apply machine learning approaches for NLP tasks, including text classification using Naïve Bayes and Logistic Regression, and learn how to evaluate models effectively.

Advanced topics include deep learning techniques such as neural networks, RNNs, LSTMs, and GRUs, as well as sequence modeling and attention mechanisms. You will also study the Transformer architecture, including self-attention and encoder–decoder models, which power modern NLP systems.

The course further introduces Large Language Models (LLMs) and generative AI, covering pre-training, fine-tuning, prompt engineering, and applications such as text generation and conversational AI. Practical applications such as sentiment analysis, named entity recognition, machine translation, and text summarization are also explored.

Finally, the course addresses ethical considerations in NLP, including bias, fairness, hallucination issues, data privacy, and responsible AI practices. By the end of the course, you will have both theoretical knowledge and practical insight into building and applying NLP systems in real-world scenarios.