Advanced NLP with Python for Machine Learning

Build upon your foundational knowledge of natural language processing by exploring more complex topics.

Introduction

• Elevate Your NLP expertise using Python and machine learning
• What you should know
• How to use the challenge exercise files

1. Introduction to NLP Libraries

• Overview of natural language processing
• Evolution of natural language processing
• Natural language processing libraries

2. Review NLP and Python Basics

• Introduction to spaCy
• Challenge: Build a spaCy processing pipeline
• Solution: Build a processing pipeline

3. Using spaCy for Customer Feedback Analysis

• Analyze customer feedback using spaCy
• The spaCy processing pipeline
• Challenge: Analyze customer feedback
• Solution: Analyze customer feedback

4. Modern NLP: Transformers and Large Language Models

• Modern natural language processing
• Transformers neural networks
• Large language models: BERT, GPT
• Challenge: Sentiment analysis using DistilBERT
• Solution: Sentiment analysis using DistilBERT

5. Methods That Improve LLM Performance

• Methods that improve LLM performance
• Supervised fine-tuning
• Fine-tuning methods
• Retrieval-augmented generation (RAG)
• Parameter-efficient fine-tuning (PEFT)
• Challenge: Parameter-efficient fine-tuning with LoRa
• Solution: Parameter-efficient fine-tuning with LoRa

Conclusion

• Next steps