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Text to Tokens: How to Implement Tokenization in NLP

BeginnerGuided Project

Tokenization is the foundation of all the real-world applications in NLP tasks such as sentiment analysis and chatbots. In this hands-on project, you’ll explore key techniques such as word, sub-word, and sentence tokenization, giving you a solid foundation for preparing text data for advanced projects. Along the way, you’ll get practical experience implementing these methods and learn how they fit into real-world scenarios. With interactive coding exercises and comparisons, you'll discover how to pick the right tokenization approach for any NLP task.

Language

  • English

Topic

  • Data Science

Skills You Will Learn

  • NLP, Python, Artificial Intelligence, Data Analysis, LLM

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • June 28, 2025
About this Guided Project
Understanding tokenization is crucial for anyone entering the field of natural language processing (NLP) because it’s the gateway to making sense of text data. Tokenization breaks down complex text into smaller, more manageable units—tokens—that models can analyze and understand. Without tokenization, it would be nearly impossible to process language data effectively, as raw text alone is unreadable for machines. Learning this skill opens doors to a wide range of NLP applications, from building chatbots and performing sentiment analysis to powering language translation and text generation systems.

This hands-on project is based on the Token optimization: The backbone of effective prompt engineering article. By completing this project, you’ll gain hands-on experience with tokenization techniques and a solid grasp of how each type works. You’ll also learn how to choose the right tokenization method based on the task at hand, setting a strong foundation for future projects in NLP, machine learning, and AI. This knowledge will not only deepen your technical skills but also empower you to tackle real-world language processing challenges with confidence.

A look at the project ahead


After completing this guided project, you will be able to:

  • Understand the importance of tokenization in NLP pipelines.
  • Learn different tokenization techniques and their applications.
  • Implement tokenization using Python libraries.
  • Apply tokenization in real-world NLP applications.

What you'll need


Before starting this guided project, it’s helpful to have a basic understanding of natural language processing (NLP) concepts and some familiarity with Python programming. This project will use Python to implement tokenization techniques, so foundational Python skills are recommended, though not required, as this project is designed to be accessible for beginners.

Instructors

Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Vicky Kuo

Data Scientist

I believe that success isn't just about individual milestones, but also about uplifting and encouraging others to reach their potential. This is why I'm passionate about combining my technical background with my eagerness to help people overcome technological hurdles and accelerate growth. When I’m not on the job, I love hiking with my two dogs or relaxing in a coffee shop. There's nothing better than having an insightful conversation over coffee, or even better, some volunteer work! Please feel free to reach out to me on LinkedIn.

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Contributors

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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