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LinkedIn Learning

Advanced NLP with Python for Machine Learning

via LinkedIn Learning

Overview

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

Syllabus

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

Taught by

Gwendolyn Stripling

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4.7 rating at LinkedIn Learning based on 42 ratings

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