Natural Language Processing Essentials

Overview

This course introduces the fundamentals of Natural Language Processing (NLP), combining core linguistic concepts with hands-on programming techniques to help you understand how machines process human language. Whether you're new to NLP or looking to build foundational skills, this course provides a clear and practical path into one of the most exciting areas of AI and data science. Through guided lessons and real-world examples, you'll learn how to clean, structure, and analyze text data, apply feature extraction techniques, and build basic NLP models for tasks like text classification and named entity recognition. By the end of this course, you will be able to: • Understand NLP basics and key language concepts like morphology, syntax, semantics, and pragmatics. • Apply text cleaning and preprocessing techniques using NLTK and SpaCy, including tokenization, stemming, lemmatization, and embeddings. • Analyze text features by extracting Bag of Words, TF-IDF, and Word2Vec representations. • Evaluate machine learning models built for text classification. • Create NLP solutions by implementing Named Entity Recognition and syntactic parsing. This course is ideal for beginners, data enthusiasts, and aspiring NLP practitioners who want to gain a strong foundation in natural language processing and its applications in AI. No prior experience with NLP is required. A basic understanding of Python or machine learning concepts will be helpful, but not mandatory. Join us to begin your journey into the world of Natural Language Processing and text analysis with Python!

Syllabus

Introduction to NLP and Lingustics

In this module, learners will develop a foundational understanding of Natural Language Processing (NLP) and its role in interpreting and processing human language. They will explore the history of NLP, its key challenges, and real-world applications. The module also introduces essential linguistic concepts—morphology, syntax, semantics, pragmatics, and discourse—that form the basis of how machines understand and work with human language.

Text Processing and Feature Engineering

This module focuses on preparing textual data for analysis by exploring techniques like tokenization, normalization, stemming, and lemmatization. Learners will also examine various feature extraction methods, including Bag-of-Words, TF-IDF, and word embeddings like Word2Vec and GloVe to represent language in machine-readable formats.

Named Entity Recognition (NER) & Parsing

In this module, learners will study techniques for identifying entities and extracting structured information from text. It covers rule-based and deep learning-based NER models, dependency and constituency parsing methods, and syntactic tree construction to enable deeper text understanding.

Course Wrap-Up and Assessment

This module is designed to assess learners on the key concepts and techniques covered throughout the course. It includes a graded quiz that tests knowledge of NLP foundations, linguistic principles, text preprocessing, feature engineering, entity recognition, and parsing methods using both classical and deep learning approaches.