The Enterprise Big Data Professional® (EBDP) course will provide delegates with a fundamental understanding of Big Data and related technologies.
AI Fundamentals
$1,100.00
Description
Course Overview
The Artificial Intelligence Fundamentals (AIFU®) certification provides a comprehensive introduction to the core principles, techniques, and enterprise applications of AI. Developed under the DASCIN® Enterprise Big Data Framework, the course offers a practical and structured pathway for anyone seeking foundational competence in modern AI. Through six interconnected modules, participants explore the building blocks of intelligent systems, beginning with rational agents and progressing into key problem-solving approaches such as search algorithms, reinforcement learning techniques, neural networks, and large language models. The program also highlights the strategic and organizational considerations required for responsible AI adoption, giving learners insight into governance, ethics, and value creation across the enterprise.
A central strength of the course is its focus on applied learning. Each module includes exercises and hands-on lab activities that transform theoretical concepts into practical skills. Participants engage with tasks such as constructing agents, experimenting with search strategies, training simple reinforcement learning models, and working with neural network components and LLM behaviors in guided environments. These labs deepen understanding and allow learners to experience how AI systems behave and evolve in practice. By combining conceptual instruction with structured application, the program ensures participants gain both clarity and confidence, equipping them to support AI-driven initiatives and to contribute effectively to modern, data-enabled organizations.
Learning Objectives
- Explain the foundations of Artificial Intelligence, including its history, goals, and the role of rational agents.
- Apply key search and problem-solving techniques such as BFS, DFS, uniform-cost search, A*, and minimax.
- Describe and interpret core reinforcement learning concepts including rewards, state transitions, the Bellman equation, and Q-learning.
- Understand the components and functioning of neural networks, including perceptrons, backpropagation, and major architectures such as CNNs, RNNs, and LSTMs.
- Analyze the structure and behavior of large language models, including transformers, self-attention, pre-training, and fine-tuning.
- Evaluate the impact of AI in organizations and apply responsible AI principles to enterprise use cases.
- Demonstrate applied learning through lab activities such as building rational agents, solving search problems, training RL agents, and designing AI enterprise use cases.
Target Audience
This certification is designed for professionals seeking practical and accessible knowledge of AI, including:
- Business and technology leaders involved in digital transformation or innovation.
- Project managers, product owners, and analysts contributing to AI initiatives.
- Data and IT professionals who want to deepen their understanding of AI without requiring advanced coding skills.
- Policy, governance, risk, and compliance professionals managing AI-related responsibilities.
- Educators, researchers, and students interested in gaining foundational AI expertise.
The course supports both technical and non-technical learners and equips them to engage confidently in AI-driven projects and enterprise initiatives.
Exam Structure
- Format: 40 multiple-choice questions
- Duration: 60 minutes (75 minutes for non-native language candidates)
- Passing Score: 65 percent (26 correct answers)
- Elevated Pass: 75 percent, required for trainers
- Exam Type: Closed book
- Bloom’s Levels: Primarily Levels 1 and 2 (recall and understanding)
Downloads and Resources
- Course Brochure - Enterprise Big Data Professional (1430 downloads )
- White Paper - Big Data in Healthcare Transforming Patient Care and Operational Efficiency (2166 downloads )
- White Paper - The Big Data Framework (2158 downloads )
If your preferred date is not available, please feel free to get in touch with us.
Additional information
| Dates | 19 – 20 January 2026 – Virtual, 30 – 31 March 2026 – Virtual, 22 – 23 June 2026, 07 – 08 September 2026 – Virtual, 23 – 24 Nov 2026 – Virtual |
|---|---|
| Course Category | Data Science |
| Exam Included | Yes |
| Duration | 3 Days |
| Format | Instructor-led |
| Exam Institute | DASCIN |
| Level | Beginner |
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