Python, NLP and GPT for the Real World

Overview

Discover how Python and GPT can revolutionize your approach to building practical NLP applications. This insightful course offers a comprehensive look into the GPT family of models and how enterprise architecture can be leveraged effectively.

Learn practical applications within manufacturing, sales, marketing, and legal fields, equipping you with the tools needed to harness the power of NLP in various industries.

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

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Subjects

Programming