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.
Natural Language Processing Essentials
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Remember key NLP concepts and terminology used in processing human language and modern AI applications.
Understand core linguistic principles like morphology, syntax, semantics, and pragmatics in NLP.
Apply Python tools and techniques to clean, preprocess, and extract features from text data effectively.
Develop and evaluate basic NLP models for tasks like text classification and named entity recognition.
Skills you'll gain
Details to know
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Recently updated!
June 2025
Assessments
15 assignments
Taught in English
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There are 4 modules in this course
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.
What's included
21 videos3 readings4 assignments1 discussion prompt
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.
What's included
44 videos4 readings6 assignments
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.
What's included
13 videos3 readings4 assignments
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.
What's included
1 video1 reading1 assignment1 discussion prompt
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Frequently asked questions
NLP (Natural Language Processing) is a branch of artificial intelligence designed to help computers understand, interpret, and generate human language. It is an extensive field with many applications, such as machine translation, chatbots, text analysis, and sentiment analysis.
The key components of NLP are:
Natural Language Understanding (NLU): The process of mapping human language input to a representation that can be understood by the computer.
Natural Language Generation (NLG): The process of generating human language output from a representation that can be understood by the computer.
Some common applications of NLP are:
Machine Translation: The process of translating text from one language to another.
Chatbots: Interactive systems that can communicate with users in natural language.
Text Analysis: The process of extracting information and insights from text data.
Sentiment analysis: Determining the emotional tone of text.
Question Answering: The development of systems that are capable of responding to inquiries regarding a specific text or knowledge base.
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