Overview
Discover how Generative AI complements traditional machine learning, when to use each technology, and how they can work together to accelerate AI development and implementation.
Syllabus
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- Introduction to AI and ML
-- Overview of Artificial Intelligence
-- Key concepts in Machine Learning
- Understanding Generative AI
-- Definition and Evolution of Generative AI
-- Key Techniques: GANs, VAEs, and Transformers
-- Use Cases of Generative AI
- Differences Between Traditional ML and Generative AI
-- Traditional Machine Learning Approaches
-- Characteristics of Generative AI
-- Comparative Analysis
- Synergies Between Traditional ML and Generative AI
-- Augmentation of Datasets with Generative AI
-- Enhancing Feature Engineering
-- Improving Model Robustness and Performance
- How Generative AI Accelerates Traditional ML
-- Workflow Integration
-- Automation of Repetitive Tasks
-- Speeding Up ML Model Iteration
- Applying Generative AI in Traditional ML Development
-- Case Studies of Successful Integrations
-- Best Practices for Hybrid AI Systems
-- Tools and Frameworks
- AI-Assisted Development
-- Role of Generative AI in Software Development
-- Enhancing Code Generation and Testing
-- Real-World Applications and Examples
- Future Trends and Challenges
-- Emerging Techniques in Generative AI
-- Ethical Considerations and Bias in Hybrid Systems
-- The Future Landscape of AI and ML Integration
- Conclusion and Next Steps
-- Summary of Key Learnings
-- Resources for Further Study
-- Opportunities for Innovation and Research
Taught by
Tags
On-Demand