Overview
Learn how to use ChatGPT to analyze data, and to build expertise in data science, math, coding, and statistics
Syllabus
-
- Introduction to Generative AI
-- Overview of AI and its societal impact
-- Key concepts in generative AI
-- Generative versus discriminative models
- Foundations of Data Analytics
-- Basic statistics and probability
-- Data types and structures
-- Introduction to data preprocessing and cleaning
- Generative Models in AI
-- Probabilistic graphical models
-- Variational autoencoders (VAEs)
-- Generative adversarial networks (GANs)
-- Diffusion models
- Applications of Generative AI in Data Analytics
-- Data augmentation and synthetic data generation
-- Anomaly detection using generative models
-- Predictive modeling enhancements
- Tools and Frameworks for Generative AI
-- Overview of popular libraries (TensorFlow, PyTorch, etc.)
-- Setting up an environment for generative AI
- Building and Training Generative Models
-- Data preprocessing for generative models
-- Model architecture design
-- Training techniques and optimization
- Evaluating Generative Models
-- Metrics for assessing model quality
-- Comparing generative and real data
-- Model validation and testing
- Advanced Topics in Generative AI
-- Ethics and biases in generative AI
-- Interpretable AI in generative models
-- Recent advancements and research directions
- Case Studies and Practical Projects
-- Real-world applications of generative AI in analytics
-- Hands-on projects with datasets
- Conclusion and Future Directions
-- Recap of key concepts
-- Future trends in generative AI and data analytics
-- Opportunities for further learning and research
Taught by
Tags
On-Demand
