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Boosting NLP performance through text augmentation

BeginnerGuided Project

Unlock powerful text augmentation techniques using Python, NLPAug, and transformers in this hands-on project. Implement Easy Data Augmentation (EDA), back-translation, and NLP augmentation with LLMs to diversify text datasets. This tutorial offers practical skills to enhance your machine learning model's robustness and performance by generating varied training data, reducing overfitting, and improving accuracy in just 45 minutes.

Language

  • English

Topic

  • Machine Learning

Skills You Will Learn

  • Text Augmentation, NLP, Python, Scikit-learn, Transformers, Generative AI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • October 15, 2025
About this Guided Project
Understanding and implementing text augmentation is a crucial skill for anyone involved in Natural Language Processing (NLP). This guided project will equip you with the tools to diversify your datasets, making your machine learning models more robust, less prone to overfitting, and ultimately more accurate. By mastering text augmentation techniques such as Easy Data Augmentation, back-translation, and text augmentation from contextual embeddings, you will be able to enhance the performance of your ML models significantly.

This project is ideal for data scientists and NLP enthusiasts who want to gain practical skills. By the end of this hands-on tutorial, you will have a deeper understanding of how to manipulate and improve text data for better model training, giving you a competitive edge in the field of machine learning.

What you'll learn


After you complete the project, you will be able to:

  • Understand the importance and impact of text augmentation in NLP.
  • Implement EDA, back-translation, and contextual text augmentation techniques using Python, NLPAug, and transformers.
  • Generate varied training data to reduce overfitting and improve the accuracy of your machine learning models.
  • Apply these augmentation techniques to real-world datasets, specifically a movie review dataset for sentiment analysis.

What you'll need


Before starting this guided project, you should have:

  • Basic knowledge of Python programming.
  • A current version of Chrome, Edge, Firefox, Internet Explorer, or Safari for the best platform experience.

Dive into this project and enhance your machine learning model's robustness and performance by mastering text augmentation techniques today!

Instructors

Fateme Akbari

Data Scientist @IBM

I'm a data-driven Ph.D. Candidate at McMaster University and a data scientist at IBM, specializing in machine learning (ML) and natural language processing (NLP). My research focuses on the application of ML in healthcare, and I have a strong record of publications that reflect my commitment to advancing this field. I thrive on tackling complex challenges and developing innovative, ML-based solutions that can make a meaningful impact—not only for humans but for all living beings. Outside of my research, I enjoy exploring nature through trekking and biking, and I love catching ball games.

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Contributors

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

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