Natural Language Processing (NLP) Online Courses & Certifications

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language. It involves the ability of a computer to understand, interpret, and generate human language in a way that is meaningful and useful.

NLP involves a combination of techniques from linguistics, machine learning, and computational algorithms to process and analyze human language. It has applications in various domains, including customer support, content analysis, language translation, information retrieval, and many more, enabling computers to understand, interpret, and generate human language in a way that is useful and meaningful.

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Codecademy Getting Started with Natural Language Processing Certificate included
For beginners
No limits
On demand
Recorded videos

From your virtual assistant recommending a restaurant to that terrible autocorrect you sent your parents, natural language processing (NLP) is a rapidly growing presence in our lives. NLP is all about how computers work with human language. Don’t just use NLP tools — make them!

Codecademy Language Parsing Certificate included
For experienced
No limits
On demand
Recorded videos

Have you ever wondered how computers break down language? This course introduces text parsing techniques using regular expressions (regex) and Part-of-Speech (POS) Tagging with NLTK.

Codecademy Learn Text Generation Certificate included
For experienced
No limits
On demand
Recorded videos

Learn how to generate and translate text using deep learning.

Codecademy Text Preprocessing Certificate included
For experienced
No limits
On demand
Recorded videos

Learn to clean text with Python 3 using regular expressions (regex) and NLTK.

DataCamp Introduction to Natural Language Processing in Python Certificate included
For beginners
No limits
On demand
Recorded videos

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