Course Outline

Introduction to Domain-Specific Language Models

  • Overview of language models in AI
  • Importance of specialization in language models
  • Case studies of successful domain-specific models

Data Curation and Preprocessing

  • Identifying and collecting domain-specific datasets
  • Data cleaning and preprocessing techniques
  • Ethical considerations in dataset creation

Model Training and Fine-Tuning

  • Introduction to transfer learning and fine-tuning
  • Selecting base models for domain-specific training
  • Techniques for effective fine-tuning

Evaluation Metrics and Model Performance

  • Metrics for domain-specific model evaluation
  • Benchmarking models against domain-specific tasks
  • Understanding limitations and trade-offs

Deployment Strategies

  • Integration of language models into domain-specific applications
  • Scalability and maintenance of deployed models
  • Continuous learning and model updates in deployment

Legal Domain Focus

  • Special considerations for legal language models
  • Case law and statute corpus for training
  • Applications in legal research and document analysis

Medical Domain Focus

  • Challenges in medical language processing
  • HIPAA compliance and data privacy
  • Use cases in medical literature review and patient interaction

Technical Domain Focus

  • Technical jargon and its implications for language models
  • Collaboration with subject matter experts
  • Technical documentation generation and code commenting

Project and Assessment

  • Project proposal and initial dataset collection
  • Presentation of a completed project and model performance
  • Final assessment and feedback

Summary and Next Steps

Requirements

  • Basic understanding of machine learning concepts
  • Familiarity with Python programming
  • Knowledge of natural language processing fundamentals

Audience

  • Data scientists
  • Machine learning engineers
 28 Hours