Text Preprocessing for Natural Language Processing (NLP)
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Text Preprocessing
Why preprocessing matters in NLP
Overview of common challenges in raw text
Use cases and impact on model performance
Module 2: Text Cleaning Techniques
Removing punctuation, numbers, and special characters
Handling whitespace, emojis, and HTML tags
Lowercasing and basic regex cleaning
Module 3: Tokenization and Sentence Segmentation
Word vs. sentence tokenization
Tokenization with NLTK, spaCy, and custom rules
Edge cases in multilingual and noisy text
Module 4: Stopword Removal and Filtering
What are stopwords and why remove them?
Using built-in vs. custom stopword lists
Domain-specific stopword tuning
Module 5: Stemming and Lemmatization
Differences between stemming and lemmatization
PorterStemmer, Snowball, and WordNet
Choosing the right method for your NLP task
Module 6: Text Normalization and Advanced Cleaning
Handling slang, contractions, and abbreviations
Removing or correcting misspellings
Normalizing text length and structure
Module 7: Capstone Project – Build a Preprocessing Pipeline
Choose a dataset (e.g., product reviews, tweets, support tickets)
Build a complete preprocessing pipeline using Python
Submit notebook with before/after examples and a documentation brief
Tools & Technologies Used:
Python, NLTK, spaCy
Regular Expressions (re module)
Jupyter Notebook or Google Colab
Optional: TextBlob, Gensim for extended tasks
Target Audience:
NLP and AI beginners
Data scientists and machine learning engineers
Developers working on chatbots or text analytics
Researchers handling unstructured text data
Global Learning Benefits:
Master essential preprocessing steps for NLP models
Improve accuracy and reliability of AI systems
Handle multilingual, noisy, and domain-specific text
Build reusable, scalable preprocessing pipelines for real projects
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