Artificial Intelligence and Machine Learning are no longer limited to technology companies. They are now influencing how organisations analyse data, automate processes, serve customers, improve decision-making and build new digital capabilities.

This bootcamp is designed to help participants understand the practical foundations of AI and ML, while also exploring modern topics such as Large Language Models, agentic systems, evaluations and coding agents. It gives professionals a structured way to understand where AI is going and how it can be applied in real business and technical environments.

This bootcamp is designed to go beyond theory. Participants will be introduced to key AI and ML concepts, but the learning journey also includes practical workflows, code walkthroughs, model evaluation, LLM API usage, agentic architecture examples, project brainstorming and participant presentations.

The programme is structured to help participants understand not only what AI is, but how AI systems are built, tested, improved and applied through real-world examples.

Unlike short awareness sessions or introductory talks, this is a 4-day immersive programme that moves progressively from fundamentals to more advanced AI concepts.

Participants begin with machine learning basics, supervised learning workflows, decision trees, random forests and neural networks. They then move into transfer learning, Large Language Models, LLM APIs, agentic architectures, deep agents, multi-agent workflows, LLM evaluations, coding agents and project demonstrations.

This gives participants a deeper and more structured learning experience.

Yes. The bootcamp is suitable for beginners to intermediate learners, professionals looking to upskill, students, technical teams, business leaders and organisations exploring AI adoption.

Technical participants will benefit from the model workflows, LLM APIs, agentic systems, evaluations and code-based examples. Business participants will benefit from understanding how AI can be applied to workflows, customer support, decision-making, automation, productivity and domain-specific use cases.

The programme is designed to bridge both understanding and application.

For companies, this bootcamp can help teams build stronger AI awareness and practical understanding of how AI systems can support business transformation.

Participants will explore concepts such as AI workflows, model evaluation, tool-using agents, customer-support architectures, domain adaptation, structured outputs and multi-agent systems. These topics are highly relevant for organisations looking to improve efficiency, automate processes, support teams and prepare for future AI adoption.

It is especially useful for organisations that want their teams to understand AI beyond basic tools and start thinking practically about implementation.

By the end of the bootcamp, participants will have a clearer understanding of how machine learning systems work, how models are evaluated, how LLMs are used, why agentic systems matter, and how AI workflows can be structured for practical use.

Participants will also engage in project brainstorming, implementation planning and presentations, helping them connect the learning to real-world ideas such as dashboarding, chatbot adaptation, maintenance prediction, asset classification and other practical applications.

The goal is to leave with stronger AI literacy, practical exposure and a clearer foundation for continued learning or implementation.

This is a limited-seat immersive bootcamp designed to maintain a focused, interactive and hands-on learning environment.

For professionals, it is an opportunity to build future-ready skills in one of the fastest-growing fields. For organisations, it is an opportunity to upskill teams in practical AI and Machine Learning through a structured programme.

The bootcamp is MQA Approved and HRDC refundable up to 90%, subject to eligibility and applicable procedures. Early registration is recommended, especially for companies planning to register multiple participants or complete internal HRDC-related processes.