Introduction to Natural Language Processing in Python
by Katharine Jarmul · DataCamp
Our Verdict
Worth it — with caveatsDataCamp's "Introduction to Natural Language Processing in Python" is a strong hands-on primer on classical NLP, but it stops short of the transformer era, so take it for foundations and not for modern LLM work. Taught by Katharine Jarmul (founder of kjamistan), it is a 4-hour, browser-based course that teaches text processing through 51 coding exercises across four chapters: regex and tokenization, bag-of-words/TF-IDF topic identification, named-entity recognition, and a capstone supervised "fake news" text classifier. It is genuinely well-regarded by completers (4.7/5 from roughly 985 ratings on DataCamp's own course page), and the learn-by-doing format with instant feedback is its biggest strength. The important caveat is recency: the course is built on NLTK, Gensim, spaCy and scikit-learn with no coverage of transformers, embeddings, or Hugging Face, and DataCamp has since published a newer course, "Natural Language Processing (NLP) in Python" (Fouad Trad, 2025), that does cover transformers and now leads its NLP skill track in place of this one. Treat it as a solid foundations course on tokenization-to-classification fundamentals, not as a path to modern transformer-based NLP.
Excellent, highly-rated, hands-on foundations for classical text processing in Python, but it predates the transformer era (NLTK/Gensim/spaCy/scikit-learn only, last content update 2024-07) and DataCamp now routes its NLP track through a newer transformer-inclusive course instead of this one. Take it if you specifically want NLP fundamentals and already have a DataCamp subscription; pair it with or skip ahead to transformer-based material if your goal is modern NLP.
Best for: Intermediate Python users (comfortable with functions, loops, and basic data structures) who want a fast, practical first exposure to classical NLP: tokenization, regex on text, bag-of-words and TF-IDF, named-entity recognition, and building a real text classifier. Ideal for analysts, students, and engineers who learn best by writing code in-browser with immediate feedback rather than watching long lectures.
Skip if: Complete Python beginners (the course assumes prior DataCamp Python coursework), and anyone whose primary goal is modern transformer/LLM-based NLP — this course does not cover BERT, embeddings, Hugging Face, or deep learning. Learners who prefer building and configuring their own local environment may also find DataCamp's locked-in browser sandbox limiting.
About This Course
Process and analyze text data using NLTK, regex, bag-of-words, and TF-IDF for text classification and topic modeling.
What You'll Learn
Curriculum
12 exercises on regex patterns, NLTK tokenization, and handling non-ASCII text.
13 exercises on bag-of-words, TF-IDF, and building a corpus/dictionary with Gensim.
11 exercises using NLTK, spaCy, and polyglot, including multilingual NER in French and Spanish.
15 exercises applying supervised learning with scikit-learn CountVectorizer/TfidfVectorizer to classify real vs. fake news articles.
Prerequisites
- Comfort with Python fundamentals: functions, loops, and basic data structures (DataCamp lists Introduction to Python, Intermediate Python, Introduction to Functions, and Python Toolbox as the assumed background)
- No prior NLP knowledge required
- No local setup needed — all coding runs in DataCamp's in-browser environment
Instructor
Katharine Jarmul
Instructor · DataCamp
Pros & Cons
Pros
- Hands-on, learn-by-doing format: 51 in-browser coding exercises with instant feedback and hints, no environment setup required
- Concrete capstone — building a working fake-news text classifier — gives a tangible, portfolio-worthy end-to-end result
- Strong, credible instructor (Katharine Jarmul, a recognized Python/NLP practitioner) and consistently high completer ratings (4.7/5 from roughly 985 ratings on DataCamp)
- Efficient scope: covers the core classical NLP toolchain (NLTK, Gensim, spaCy, polyglot, scikit-learn) in roughly 4 hours
- Earns a shareable Statement of Accomplishment on completion
Cons
- No coverage of modern NLP: transformers, word/sentence embeddings, BERT, or Hugging Face are entirely absent — it is a classical/pre-deep-learning curriculum
- Content is dated (last updated 2024-07) and DataCamp has since published a newer transformer-inclusive course that now leads its NLP track rather than this one
- Locked into DataCamp's browser sandbox, so learners don't practice setting up or managing a real local NLP environment
- Only the first chapter is accessible on the free plan; full access requires a paid subscription
Alternatives To Consider
Frequently Asked Questions
Is Introduction to Natural Language Processing in Python free?
Introduction to Natural Language Processing in Python is $25/mo. Requires a DataCamp Premium subscription: about $25/month month-to-month, or roughly $149/year (~$12.42/month) billed annually. A free DataCamp account unlocks only the first chapter; the certificate (Statement of Accomplishment) and remaining chapters need a paid plan. Prices vary by region and frequent promotions.
Who is Introduction to Natural Language Processing in Python for?
Intermediate Python users (comfortable with functions, loops, and basic data structures) who want a fast, practical first exposure to classical NLP: tokenization, regex on text, bag-of-words and TF-IDF, named-entity recognition, and building a real text classifier. Ideal for analysts, students, and engineers who learn best by writing code in-browser with immediate feedback rather than watching long lectures.
What will you learn in Introduction to Natural Language Processing in Python?
Write regular expressions and tokenize text with NLTK, including handling non-ASCII/multilingual input; Build bag-of-words and TF-IDF representations and do simple topic identification, including using Gensim to create a corpus and dictionary; Perform named-entity recognition with NLTK, and compare approaches using spaCy and polyglot (including NER in French and Spanish); Apply supervised machine learning (scikit-learn CountVectorizer and TfidfVectorizer with Naive Bayes) to build and evaluate a 'fake news' text classifier.
What are the prerequisites for Introduction to Natural Language Processing in Python?
Comfort with Python fundamentals: functions, loops, and basic data structures (DataCamp lists Introduction to Python, Intermediate Python, Introduction to Functions, and Python Toolbox as the assumed background); No prior NLP knowledge required; No local setup needed — all coding runs in DataCamp's in-browser environment.
Is Introduction to Natural Language Processing in Python worth it?
Excellent, highly-rated, hands-on foundations for classical text processing in Python, but it predates the transformer era (NLTK/Gensim/spaCy/scikit-learn only, last content update 2024-07) and DataCamp now routes its NLP track through a newer transformer-inclusive course instead of this one. Take it if you specifically want NLP fundamentals and already have a DataCamp subscription; pair it with or skip ahead to transformer-based material if your goal is modern NLP.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on DataCamp's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official DataCamp course page (syllabus, chapters, instructor, 4.7/5 rating from ~985 ratings)
- DataCamp's newer transformer-inclusive course that now leads the NLP track (Natural Language Processing (NLP) in Python, Fouad Trad, 2025)
- DataCamp NLP in Python skill track (this intro course no longer leads the track)
- Class Central listing (independent third-party listing for this course)
- DataCamp Review 2026: Is $25/Month Worth It (pricing reference)