The Certified Artificial Intelligence Professional course helps participants gain the knowledge and skills required to excel in AI related roles. The program includes a broad range of domains from foundational AI concepts to advanced applications like machine learning, deep learning, natural language processing, robotics, computer vision and expert systems.
It also emphasises responsible AI practices, including risk management, ethics, and compliance, ensuring participants are well-prepared to implement AI solutions in real-world scenarios.
Fees
COURSE OUTLINES
Course Agenda
- Day 1: Foundations of AI and Data Analysis
- Day 2: Machine Learning
- Day 3: Deep Learning and Natural Language Processing
- Day 4: Computer Vision, Robotics, AI Strategy, Governance, and Risk Management
- Day 5: Certification exam
Examination
The “PECB Certified Artificial Intelligence Professional” exam meets the requirements of the PECB Examination and Certification Program (ECP). It covers the following competency domains:
- Domain 1: Fundamental principles and concepts of an artificial intelligence management system
- Domain 2: Apply data analysis and visualization
- Domain 3: Develop Machine Learning models using Python
- Domain 4: Establish governance frameworks to integrate AI
COURSE DETAILS
Duration and Access
- Duration: Up to 6 months
- Starts: Upon Registration
- Ends: After Examination
You will be enrolled on the PECB platform KATE, where you will have access to all relevant training procedures.
Certification fees are included in the price of the exam.
In case of exam failure, you may retake the exam once within 12 months at no additional cost. This is included in the original training fee. Subsequent retakes are subject to additional fees.
Learning Objectives
By the end of this training course, the participants will be able to:
- Explain the foundational principles of AI and its various applications.
- Conduct data analysis and create meaningful visualizations to support AI projects.
- Apply machine learning techniques to real-world problems, including supervised, unsupervised, and reinforcement learning.
- Implement simple neural network and advanced deep learning architectures such as CNNs.
- Understand NLP systems and Computer Vision methodologies.
- Understand robotics and expert systems for AI-driven automation.
- Identify and mitigate AI risks while ensuring compliance with regulations.
- Develop ethical AI strategies aligned with organizational values and societal needs.
PREREQUISITES
A general understanding of basic programming knowledge is recommended.