About Certified AI Practitioner™ (CAIP)
This course gives technology, data, and engineering professionals a deep technical foundation in core AI disciplines, going well beyond general AI awareness into genuine practitioner-level capability. It covers intelligent agents, machine learning, fuzzy logic, and genetic algorithms, grounded in the fundamentals of AI reasoning and knowledge representation.
You'll start with an overview of AI, its history, and the role of intelligent agents, then move into agent architecture, including knowledge-base structures and logic reasoning. From there, the course covers machine learning techniques like classification, clustering, and neural networks, followed by fuzzy logic and building your own tiny machine learning application, and finishing with genetic algorithms applied to real business optimisation problems.
By the end, you'll have hands-on experience building a machine learning application and applying a genetic algorithm to a real business optimisation example.
Expected Outcomes
The course is structured around five technical disciplines — from AI fundamentals through to genetic algorithms — so each module builds real practitioner-level capability. By the end, you'll be able to:
- Explain the history and evolution of AI and distinguish human from artificial intelligence
- Describe intelligent agent types, knowledge-base structures, and logic reasoning mechanisms
- Apply supervised and unsupervised learning techniques, including classification and neural networks
- Apply object recognition principles and evaluate features and classes in model development
- Apply fuzzy sets and fuzzy rules to real control problems
- Build a working machine learning application using practical tools
- Explain genetic algorithm structure, including chromosomes, selection, and mutation
- Apply genetic algorithms to real business process optimisation problems
Training Method
The course begins with an overview of AI history and intelligent agent theory, before moving into agent architecture, covering knowledge-base structures, logic reasoning, and deduction processes. It then covers machine learning in depth, including supervised and unsupervised learning, classification, clustering, and neural networks.
From there, you'll explore fuzzy logic, including fuzzy sets and rules applied to real controllers, and build a tiny machine learning application yourself. The course closes with genetic algorithms, covering how they work and evolve, before applying them to a real business process optimisation example. Throughout, you'll work through instructor-led technical sessions, hands-on workshops, and practical exercises applying techniques to real datasets and business problems, so you leave with genuine practitioner-level AI capability.
