NLP: Fine-Tune & Preprocess Text

Overview

Did you know that 80% of the world's data is unstructured text? Yet most organizations struggle to extract actionable insights from this goldmine of information. This Short Course was created to help machine learning and AI professionals accomplish domain-specific natural language processing through systematic model adaptation and robust text preprocessing workflows. By completing this course, you'll be able to fine-tune BERT models on specialized datasets, build automated spaCy pipelines for text standardization, and deploy production-ready NLP solutions that deliver measurable performance improvements in your next project. By the end of this course, you will be able to: - Create fine-tuned transformer language models for domain-specific applications - Apply text preprocessing techniques to build a pipeline for cleaning and standardizing raw text This course is unique because it combines hands-on fine-tuning with Hugging Face Trainer and practical pipeline construction using spaCy, giving you immediately applicable skills for real-world NLP challenges. To be successful in this project, you should have a background in Python programming, basic machine learning concepts, and familiarity with transformer architectures.

Syllabus

Module 1: Fine-Tuning Transformer Language Models

Learners will master the process of adapting pre-trained BERT models for specialized domains using Hugging Face Transformers, achieving production-ready performance on domain-specific tasks.

Module 2: Text Preprocessing Pipeline Development

Learners will build comprehensive text preprocessing pipelines using spaCy that transform raw text into analysis-ready formats through systematic tokenization, normalization, and encoding workflows.