Day 1 covers AI fundamentals and data analysis; Day 2 focuses on machine learning; Day 3 covers deep learning and NLP; Day 4 covers computer vision, robotics, and responsible AI strategy, governance, and risk.
Day 1 establishes foundations: AI concepts and principles plus data analysis and visualization—critical for framing and validating AI projects.
Day 2 focuses on machine learning: workflow plus supervised and unsupervised learning, then broader applications and advanced ML considerations.
Day 3 deepens capability with NLP and deep learning: from classical NLP techniques to modern Transformers and large language models, plus deep learning architectures and advanced techniques.
Day 4 expands into applied domains and responsible delivery: computer vision and robotics, as well as AI security, ethics, governance, and strategy to ensure AI systems are deployed responsibly and aligned to organizational objectives.
If you connect each day to an AI lifecycle step—data, modeling, deployment, and governance—you'll retain the material faster and be better prepared for domain-based exam questions.
“The course moves from foundations to deployment-ready capability.”
Expert Trainer
Expert Trainer
The CAIP exam is domain-based, covering AI fundamentals, data analysis, ML, deep learning and NLP, computer vision and robotics, plus AI risk, privacy, compliance, ethics, governance, and strategy.
Machine learning learns patterns from data. Deep learning uses neural networks for complex representations. NLP applies these techniques to language understanding and generation.
A CAIP professional designs and deploys AI solutions, validates models with data, and manages risk, ethics, privacy, and governance so AI delivers value responsibly.
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