Course description

Master the basics of Regular Expressions (Regex) and discover how they play a powerful role in Natural Language Processing (NLP)!
In this course, we break down how regex helps in text cleaning, pattern matching, tokenization, and extracting meaningful information from raw text.

What you’ll learn:
• What Regular Expressions are
• Common regex patterns and syntax
• How regex is used in NLP tasks
• Practical examples for text processing
• Why mastering regex is essential for NLP projects

Perfect for Python learners, NLP beginners, and anyone looking to boost their text-processing skills.
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What will i learn?

• Review the history and evolution of NLP techniques and applications, from traditional machine learning models to modern LLM approaches
• Use traditional machine learning techniques to perform sentiment analysis, text classification, and topic modeling
• Break down the main parts of the Transformers architecture, including embeddings, attention and feedforward neural networks (FFNs)
• Walk through the NLP text preprocessing pipeline, including cleaning, normalization, linguistic analysis, and vectorization
• Understand the theory behind neural networks and deep learning, the building blocks of modern NLP techniques
• Use pretrained LLMs with Hugging Face to perform sentiment analysis, NER, zero-shot classification, document similarity, and text summarization & generation

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