Natural Language Processing

Course Overview

Natural Language Processing (NLP) is an advanced, application‑driven course that explores how machines understand, interpret, and generate human language. Learners work with real text data to build NLP pipelines, apply linguistic preprocessing, and implement state‑of‑the‑art models for tasks such as classification, sentiment analysis, topic modeling, and text generation. The course blends theory, linguistics, and hands‑on implementation using Python and modern NLP libraries.

Target Audience

This course is ideal for:

• Data scientists, ML engineers, and AI practitioners

• Analysts and developers working with text‑heavy datasets

• Students or professionals preparing for careers in AI, NLP, or language‑centric applications

• Anyone with prior Python and machine‑learning knowledge looking to specialize in language technologies

Course Outcomes

By the end of this course, learners will be able to:

• Understand key NLP concepts: tokenization, stemming, lemmatization, embeddings, and language modeling

• Build NLP pipelines using libraries like NLTK, spaCy, and Hugging Face Transformers

• Apply techniques such as text classification, sentiment analysis, named‑entity recognition, and topic modeling

• Work with word embeddings (Word2Vec, GloVe) and contextual models (BERT, GPT‑style architectures)

• Preprocess and clean unstructured text data for downstream tasks

• Evaluate NLP models using appropriate metrics and interpret their outputs

• Apply NLP techniques to real‑world business and research scenarios