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Advanced NLP with Python for Machine Learning

سرفصل های دوره

This course is for anyone who wants to learn more advanced NLP methods. Instructor Gwendolyn Stripling, PhD, begins with a look at the fundamental concepts and principles of NLP, including the evolution and significance of natural language processing. She then reviews some NLP and Python basics—and introduces the NLP library spaCy—before jumping into more modern techniques and advancements in natural language processing using Transformer Models like GPT and BERT. Methods such as supervised fine-tuning, parameter efficient fine-tuning (PEFT), and retrieval-augmented generation (RAG) give you the foundational knowledge you need to improve large language model (LLM) performance. Learn the ways you can apply NLP in your applications and day-to-day, including how to analyze customer sentiments Each chapter ends with a challenge and solution, so you can test your knowledge as you go.


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

  • 02 - 1. Introduction to NLP Libraries
  • 01 - Overview of natural language processing
  • 02 - Evolution of natural language processing
  • 03 - Natural language processing libraries

  • 03 - 2. Review NLP and Python Basics
  • 01 - Introduction to spaCy
  • 02 - Challenge Build a spaCy processing pipeline
  • 03 - Solution Build a processing pipeline

  • 04 - 3. Using spaCy for Customer Feedback Analysis
  • 01 - Analyze customer feedback using spaCy
  • 02 - The spaCy processing pipeline
  • 03 - Challenge Analyze customer feedback
  • 04 - Solution Analyze customer feedback

  • 05 - 4. Modern NLP Transformers and Large Language Models
  • 01 - Modern natural language processing
  • 02 - Transformers neural networks
  • 03 - Large language models BERT, GPT
  • 04 - Challenge Sentiment analysis using DistilBERT
  • 05 - Solution Sentiment analysis using DistilBERT

  • 06 - 5. Methods That Improve LLM Performance
  • 01 - Methods that improve LLM performance
  • 02 - Supervised fine-tuning
  • 03 - Fine-tuning methods
  • 04 - Retrieval-augmented generation (RAG)
  • 05 - Parameter-efficient fine-tuning (PEFT)
  • 06 - Challenge Parameter-efficient fine-tuning with LoRa
  • 07 - Solution Parameter-efficient fine-tuning with LoRa

  • 07 - Conclusion
  • 01 - Next steps
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    تاریخ انتشار: 22 مرداد 1403
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