What topics are covered across the four course days?
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.
Related Information
- Day 1: AI fundamentals + data analysis/visualization.
- Day 2: ML workflow, supervised/unsupervised learning.
- Day 3: NLP (Transformers/LLMs) + deep learning architectures.
- Day 4: vision, robotics, security, ethics, governance, strategy.
- Hands-on exercises support applied understanding.
Expert Insight
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.”
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