Python, NLP and GPT for the Real World

Overview

Explore Python and GPT for building real-world NLP applications with insights into GPT family tree, enterprise architecture, and NLP applications in manufacturing, sales, marketing and legal.

Syllabus

• Introduction to Python for NLP
Basics of Python programming
Python libraries for NLP (e.g., NLTK, spaCy, gensim)
Introduction to Jupyter Notebooks
• Fundamentals of Natural Language Processing (NLP)
Overview of NLP techniques
Text preprocessing (tokenization, stemming, lemmatization)
Named Entity Recognition (NER)
• GPT and the Transformer Architecture
Introduction to the Transformer model
Overview of the GPT family tree
Differences between GPT-2, GPT-3, and GPT-4
• Leveraging GPT for NLP Applications
Text generation with GPT
Building chatbots and conversational agents
Sentiment analysis using pre-trained models
• Enterprise Architecture for NLP Solutions
Designing scalable NLP architectures
Cloud solutions for deploying NLP applications
Considerations for data privacy and security in NLP
• Real-World NLP Applications
NLP in manufacturing: predictive maintenance and quality control
NLP in sales and marketing: customer insights and personalization
NLP in legal: document review and contract analysis
• Case Studies and Implementation
End-to-end implementation of an NLP project
Case study: automated report generation
Case study: customer feedback analysis with GPT
• Final Project
Design and deploy a real-world NLP application
Presentation and peer review of projects
• Course Wrap-Up and Future Directions
Recap of key concepts and tools
Emerging trends in NLP and GPT research
Next steps for continued learning and exploration in AI and NLP

Subjects

Programming