Academic

HOME/Academic/Professional Programmes/Professional Certificate in Applied Artificial Intelligence

Professional Certificate in Applied Artificial Intelligence

Programme Overview


Duration


MQA Accredited


Brochure


Online Application


Enquiry

The Professional Certificate in Applied Artificial Intelligence (AI) is designed to enhance participants’ expertise in implementing and managing AI and Machine Learning (ML) systems. The course covers fundamental definitions, applications, AI/ML approaches, tools, and techniques. It begins with an introduction to AI and ML, highlighting key concepts and historical developments that have shaped current practices. Participants will gain a deep understanding of AI/ML principles, including algorithmic approaches, constraints, and the use of various tools such as Rapidminer, Python, and Octave. The course also addresses compliance with industry standards and best practices, ensuring participants are well-versed in legal and ethical requirements. Practical skills covered include implementing machine learning modules, developing simple ML applications using Python, applying deep learning techniques, and utilizing Natural Language Processing (NLP). The course also focuses on problem-solving techniques such as logic rules, fuzzy expert systems, and evolutionary algorithms. By the end of the course, participants will be equipped to develop and maintain effective AI/ML systems, conduct data analytics, ensure ethical compliance, and apply AI solutions to
real-world business challenges. Successful completion leads to certification, recognizing the individual’s competence in applied artificial intelligence and machine learning.

Programme StructureProgramme Outcome

Programme Structure

Programme Duration:
Total Duration: 10 weeks
Schedule: 3 hours per day, 6 days a week

Weeks Days Subject
Week 1: Introduction to AI and Machine Learning Day 1-3
  • Introduction to Artificial Intelligence
    • Definitions and Applications
    • Historical Development and Key Figures
  • Basic Concepts in Machine Learning
    • Supervised vs. Unsupervised Learning
Day 4-6
  • Overview of AI/ML Approaches
    • Algorithms and Constraints
    • Ethical and Legal Considerations
Week 2: Rapidminer Essentials Day 1-3
  • Introduction to Rapidminer
    • Environment and Interface
    • Basic Features and Functions
Day 4-6
  • Practical Exercises with Rapidminer
    • Data Import and Preprocessing
    • Basic Data Analytics
Week 3: Machine Learning with Rapidminer Day 1-3
  • Designing ML Systems in Rapidminer
    • Building and Training Models
    • Supervised Learning Algorithms
Day 4-6
  • Implementing ML Systems
    • Evaluation and Testing
    • Model Optimization
Week 4: Python for Machine Learning Day 1-3
  • Introduction to Python Programming
    • Basics of Python Syntax
    • Data Structures and Libraries
Day 4-6
  • Python for ML
    • Developing Simple ML Modules
    • Practical Exercises and Projects
Week 5: Deep Learning Basics Day 1-3
  • Introduction to Deep Learning
    • Basic Concepts and Applications
    • Neural Networks
Day 4-6
  • Implementing Deep Learning Models
    • Using TensorFlow and Keras
    • Practical Exercises
Week 1: Introduction to AI and Machine Learning Day 1-3
  • Introduction to Artificial Intelligence
    • Definitions and Applications
    • Historical Development and Key Figures
  • Basic Concepts in Machine Learning
    • Supervised vs. Unsupervised Learning
Day 4-6
  • Overview of AI/ML Approaches
    • Algorithms and Constraints
    • Ethical and Legal Considerations
Week 2: Rapidminer Essentials Day 1-3
  • Introduction to Rapidminer
    • Environment and Interface
    • Basic Features and Functions
Day 4-6
  • Practical Exercises with Rapidminer
    • Data Import and Preprocessing
    • Basic Data Analytics
Week 3: Machine Learning with Rapidminer Day 1-3
  • Designing ML Systems in Rapidminer
    • Building and Training Models
    • Supervised Learning Algorithms
Day 4-6
  • Implementing ML Systems
    • Evaluation and Testing
    • Model Optimization
Week 4: Python for Machine Learning Day 1-3
  • Introduction to Python Programming
    • Basics of Python Syntax
    • Data Structures and Libraries
Day 4-6
  • Python for ML
    • Developing Simple ML Modules
    • Practical Exercises and Projects
Week 5: Deep Learning Basics Day 1-3
  • Introduction to Deep Learning
    • Basic Concepts and Applications
    • Neural Networks
Day 4-6
  • Implementing Deep Learning Models
    • Using TensorFlow and Keras
    • Practical Exercises

Programme Outcome

Upon successful completion of this 10-week intensive course, participants will be able to:

  1. Articulate AI and ML Fundamentals:
    • Define and explain the basic concepts, historical development, and applications of Artificial Intelligence and Machine Learning
  2. Implement AI/MLAlgorithms:
    • Understand various AI/ML approaches and constraints, and demonstrate how algorithms function in practical scenarios.
  3. Utilize Rapidminer for Machine Learning:
    • Navigate and effectively use the Rapidminer environment to design, implement, and evaluate machine learning systems.
  4. Develop ML Modules Using Python:
    • Write and execute Python code to develop simple machine learning modules, utilizing essential libraries and data structures.
  5. Comprehend Deep Learning Concepts:
    • Understand basic to advanced deep learning techniques, including neural networks, and apply these concepts in various business contexts.
  6. Apply Natural Language Processing Techniques:
    • Demonstrate an understanding of NLP basics and develop simple NLP systems using deep learning algorithms.
  7. Perform Data Analytics Using Octave:
    • Utilize Octave for matrix computation, data analytics, and machine learning, developing efficient programs and solutions.
    • Design and Implement AI Solutions:
      • Design rules-based fuzzy expert systems and evolutionary algorithms for solving optimization problems.
    • Integrate AI into Business Solutions:
      • Identify and implement AI applications in real-world business scenarios, assessing their impact and optimizing business processes.
    • Develop Comprehensive AI Projects:
      • Plan, develop, and present comprehensive AI solutions through capstone projects, demonstrating the integration of learned techniques and tools.
    • Ensure Ethical and Legal Compliance:
      • Understand and apply ethical guidelines and legal requirements related to AI/ML implementation in business contexts.
    • Promote a Culture of AI-Driven Innovation:
      • Foster a culture of innovation and productivity within organizations by leveraging AI and ML technologies to address business challenges.

Participants will leave the course equipped with practical skills, hands-on experience, and a recognized certification, positioning them as competent professionals in the field of applied artificial intelligence.