1. Foundations of Predictive AI

• Predictive AI business and technology drivers

• Key benefits of Predictive AI in enterprise environments

• Common risks and challenges of using Predictive AI

• Business problem categories addressed by AI systems

• Types of Predictive AI and where they are applied

• Common learning approaches:

  • Supervised learning

  • Unsupervised learning

  • Reinforcement learning

  • Continuous and semi-supervised learning

• Understanding predictive model training and the step-by-step training loop

• Functional applications of Predictive AI, including:

  • Computer vision

  • Pattern recognition

  • Robotics

  • Natural language processing (NLP)

  • Speech recognition and understanding (NLU)

• Introduction to AI models and neural networks

2. Foundations of Generative AI

• Generative AI business and technology drivers

• Benefits and opportunities enabled by Generative AI

• Common risks and challenges of using Generative AI

• Business problem categories addressed by Generative AI solutions

• Understanding models, algorithms, and neural network fundamentals

• Types of Generative AI systems

• Training generative models and understanding the training loop

• Key Generative AI architectures:

  • Generative Adversarial Networks (GANs)

  • Variational Encoders (VAEs)

  • Transformer models

• Steps to building AI-driven systems

• Generative AI best practices for real-world adoption