CERTIFIED ARTIFICIAL INTELLIGENCE ENGINEER (CAIE™)

Program Curriculum

According to the latest studies done by USAII^®, Artificial Intelligence’s demand is increasing at a rapid pace with new roles such as AI/ML developer, AI/ML engineer, AI/ML architect, AI/ML consultant, AI/ML scientist, and more. Therefore, at USAII^®, we analyzed the trends and concluded that candidates with the right knowledge and skills could choose to take on any demanding role, such as an AI Engineer, AI Developer and more. The program curriculum below is meticulously planned and designed to counter any demands of an emerging AI Engineer/Developer.

• Exploring AI and ML Essentials 12%
  • Essentials for ML Engineers
  • Machine Learning Process
  • Movie Recommendation Engine with Naïve Bayes
  • Ad Clicks with Logistic Regression
  • Stock Price Prediction Using Regression Techniques
• Engineering the Future: From Debugging to Robotics 15%
  • Debugging for Responsible AI
  • MLOps: The Backbone of Scalable AI Systems
  • Robotics and AI: Building the Basics
  • Configuring Your Robot
• Deep Learning Concepts: Advanced 17%
  • Life Cycle of Deep Learning Models
  • Exploring CNNs and RNNs
  • Decoding Autoencoders and Transformers
  • Making Sense of Neural Networks
• Natural Language Processing Demystified 13%
  • Getting Started with NLP
  • Leveraging ML for Natural Language Processing
  • Streamlining NLP Text Processing
  • Classifying Text with Traditional ML Techniques
• Theories of Computer Vision and GANs 15%
  • The Building Blocks of Neural Networks
  • Building Deep Learning Models with PyTorch
  • Exploring Convolutional Neural Networks
  • Image Classification with Transfer Learning
  • Object Detection: Concepts and Techniques
• Generative AI and LLMs: Theory and Training 7%
  • Introduction to Generative AI
  • From GANs to Transformers: Generative AI Models
  • Large Language Models
  • LLM Architecture
• Advanced LLMs: Techniques, Applications, and Frameworks 8%
  • Optimizing Large Language Models
  • Innovative Applications of LLMs
  • Matching LLMs to Applications
  • Prompt Engineering: Techniques and Best Practices
  • Tools and Frameworks for LLMs
  • The Future of LLMs: GPT-5 and Beyond
  • LLM Integration for Applications
  • Creating Conversational AI with LLMs
• Mastering Retrieval-Augmented Generation 7%
  • Retrieval-Augmented Generation (RAG)
  • Developing a Complete RAG Pipeline
  • RAG in Practice: Use Cases and Solutions
  • RAG Architecture
  • Integrating RAG with AI Agents and LangGraph
• Navigating ML: Architectures, Tools, and Platforms 6%
  • ML Systems: The Lifecycle and Architecture
  • Business Use Cases for Machine Learning
  • Breaking Down ML Algorithms
  • Managing Data for Machine Learning Success
  • Exploring the Best ML Libraries
  • The Power of Open-Source ML Platforms