Natural Language Processing (NLP)

Introduction

Natural Language Processing (NLP) is a key branch of Artificial Intelligence that enables machines to understand, interpret, and generate human language. From sentiment analysis and text classification to machine translation and text generation, NLP powers many of today’s intelligent applications such as chatbots, virtual assistants, and search engines. This course is designed to provide a structured and practical introduction to NLP, combining linguistic concepts with modern deep learning techniques.

The program begins with the basics of neural networks, gradually progressing to recurrent neural networks (RNNs) and their different variants. Learners will explore advanced sequence models such as LSTM and GRU, understanding how they address long-term dependency challenges in text data. The course emphasizes the complete NLP workflow, starting from data collection and preprocessing to preparing text for machine learning and deep learning models.

Core text representation techniques including Bag of Words, TF-IDF, and word embeddings are covered, followed by classical NLP methods such as text classification using the Naive Bayes algorithm. Participants will then apply deep learning models to real-world NLP tasks such as sentiment analysis and part-of-speech (POS) tagging using LSTM networks.

The curriculum also introduces encoder–decoder architectures, enabling learners to build text generation and machine translation models using LSTM. To align with modern NLP advancements, the course provides an overview of the Transformer architecture, along with an introduction to BERT and its impact on contextual language understanding. Finally, learners will explore Hugging Face and its pre-trained models, gaining exposure to industry-standard tools used for building and deploying state-of-the-art NLP solutions.

By the end of this course, participants will have a strong conceptual foundation and practical understanding of NLP techniques—from traditional approaches to modern deep learning and transformer-based models—empowering them to build intelligent language-based applications with confidence.