
Course overview & learning outcomes
Explore real-world NLP capabilities through the movie Her
Map Samantha's abilities to core NLP concepts and techniques
Understand NLP with examples
Learn ambiguity, context, and sentiment in human language
Identify NLP applications used in daily life and digital products
Explore use cases such as sentiment analysis and topic modeling
Learn tokenization, stopword removal, preprocessing, and the NLP workflow
Compare stemming and lemmatization with practical examples
Tokenization, Lemmatization & Stemming - example walkthrough
Convert text into numerical features for machine learning
Extract grammatical structure and identify entities from text
Understand the business problem and NLP solution approach
Build and execute an end-to-end NLP solution in Python
Explore emerging NLP trends, LLMs, and autonomous systems
Every day, businesses generate massive amounts of text—from customer reviews and emails to support tickets, surveys, and social media posts. Natural Language Processing (NLP) enables us to transform this unstructured text into meaningful insights that drive better decisions.
In this beginner-friendly course, you'll learn the core concepts of Natural Language Processing using practical examples and hands-on Python implementation. Rather than diving into complex mathematics, the course focuses on building strong intuition and applying NLP techniques to real-world business problems.
We'll start by understanding what NLP is, why it has become one of the most important areas of Artificial Intelligence, and where it is used in everyday applications. Using relatable examples and popular movies, you'll discover how computers interpret and understand human language.
Next, you'll master the complete NLP pipeline, including text preprocessing techniques such as tokenization, stopword removal, stemming, and lemmatization. You'll then learn how text is converted into numerical features using Bag of Words and TF-IDF.
The course also covers two of the most widely used NLP techniques—Part-of-Speech (POS) Tagging and Named Entity Recognition (NER)—to understand sentence structure and identify important entities from text.
Finally, you'll apply everything you've learned by building a customer analytics case study using Python, demonstrating how NLP can uncover business insights from customer reviews.
What you'll learn
Understand the complete NLP workflow
Clean and preprocess text data
Apply stemming, lemmatization, POS tagging, and NER
Convert text into machine learning features using Bag of Words and TF-IDF
Build an end-to-end NLP project using Python
Understand practical NLP applications across industries
Whether you're a student, aspiring data scientist, AI enthusiast, analyst, or business professional, this course will give you a strong foundation in Natural Language Processing and prepare you to explore advanced NLP and Generative AI topics with confidence.