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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
