Skip to main content

Introduction to Natural Language Processing in Python

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Start Course for Free
4 Hours15 Videos51 Exercises79,624 Learners
3750 XP

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

In this course, you'll learn natural language processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.

  1. 1

    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.

    Play Chapter Now
    Introduction to regular expressions
    50 xp
    Which pattern?
    50 xp
    Practicing regular expressions: re.split() and re.findall()
    100 xp
    Introduction to tokenization
    50 xp
    Word tokenization with NLTK
    100 xp
    More regex with
    100 xp
    Advanced tokenization with NLTK and regex
    50 xp
    Choosing a tokenizer
    50 xp
    Regex with NLTK tokenization
    100 xp
    Non-ascii tokenization
    100 xp
    Charting word length with NLTK
    50 xp
    Charting practice
    100 xp
  2. 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.

    Play Chapter Now
  3. 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.

    Play Chapter Now

In the following tracks

Machine Learning ScientistNatural Language Processing


Hugo Bowne-AndersonYashas Roy
Katharine Jarmul Headshot

Katharine Jarmul

Founder, kjamistan

Katharine Jarmul runs a data analysis company called kjamistan that specializes in helping companies analyze data and training others on data analysis best practices, particularly with Python. She has been using Python for 8 years for a variety of data work -- including telling stories at major national newspapers, building large scale aggregation software, making decisions based on customer analytics, and marketing spend and advising new ventures on the competitive landscape.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA