Course
Introduction to Natural Language Processing in Python
IntermediateSkill Level
Updated 02/2026PythonMachine Learning4 hr15 videos51 Exercises3,750 XP140K+Statement of Accomplishment
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Prerequisites
Python Toolbox1
Regular expressions & word tokenization
This chapter will introduce some basic NLP concepts, such as word tokenization and regular expressions to help parse text. You'll also learn how to handle non-English text and more difficult tokenization you might find.
2
Simple topic identification
This chapter will introduce you to topic identification, which you can apply to any text you encounter in the wild. Using basic NLP models, you will identify topics from texts based on term frequencies. You'll experiment and compare two simple methods: bag-of-words and Tf-idf using NLTK, and a new library Gensim.
3
Named-entity recognition
This chapter will introduce a slightly more advanced topic: named-entity recognition. You'll learn how to identify the who, what, and where of your texts using pre-trained models on English and non-English text. You'll also learn how to use some new libraries, polyglot and spaCy, to add to your NLP toolbox.
4
Building a "fake news" classifier
You'll apply the basics of what you've learned along with some supervised machine learning to build a "fake news" detector. You'll begin by learning the basics of supervised machine learning, and then move forward by choosing a few important features and testing ideas to identify and classify fake news articles.
Introduction to Natural Language Processing in Python
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FAQs
Which NLP libraries will I learn to use in this course?
You will work with NLTK, Gensim, spaCy, and polyglot to perform tokenization, topic identification, named-entity recognition, and text classification tasks.
What prior Python knowledge is required?
You should have completed Introduction to Python, Intermediate Python, Introduction to Functions, and Python Toolbox. Comfort with functions, loops, and basic data structures is needed.
What is the fake news classifier project in the final chapter?
You will apply supervised machine learning to build a classifier that distinguishes fake news articles from real ones, selecting features and evaluating model performance on text data.
Does this course handle non-English text?
Yes. The first chapter covers tokenization challenges with non-English text, and the named-entity recognition chapter uses polyglot to process text in multiple languages.
What topic identification methods are compared?
You will experiment with bag-of-words and TF-IDF approaches using NLTK and Gensim to identify topics based on term frequencies, then compare how each method performs.
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