AI & Machine Learning Courses | Learn AI & ML at IEST

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IEST – AI & Machine Learning assessment helps individuals understand foundational knowledge with simple, real-world examples and questions designed for beginners. The test adjusts to the test-taker’s skill level, ensuring a comfortable and engaging experience that evaluates your understanding of key AI/ML concepts.

This quick and user-friendly assessment covers basic AI/ML topics and is designed to help you identify your strengths and areas for improvement.

The test is mapped to the Common International Framework of Reference (CIFR) for Future Readiness and provides instant results along with ‘can-do’ statements to provide clarity on the level of understanding of the test-taker

Test Format

Online Test

Duration: 30 minutes
Total Questions: 50

Variety of Task Types:

Multiple Choice Questions (MCQs) for:
- Basic AI/ML concepts and definitions
- Common AI/ML applications
- Fundamentals of algorithms and models
- Ethical considerations in AI
True/False/Not Given for:
- General AI/ML facts and misconceptions
- Ethical and responsible AI practices
Fill in the Blanks to assess:
- AI/ML terminology and key concepts
- Steps in the AI/ML process
Match the Following to test:
- Basic AI/ML tools and their functions
- Common terms with their meanings
Basic Identification Questions for:
- Recognizing AI in everyday life
- Differentiating AI and non-AI application

Assessment

AI/ML Concepts:The ability to understand fundamental AI/ML principles, such as machine learning types, algorithms, and their basic applications across different industries.

Data Understanding: The knowledge of data types, sources, and their role in AI/ML, including basic data preprocessing and quality considerations.

Algorithms and Models: The ability to identify common AI/ML algorithms, their functions, and when to use them based on problem types and data availability.

Ethical AI: Understanding the ethical considerations of AI, including fairness, bias, privacy concerns, and responsible usage in everyday applications.

AI Applications: Recognising AI-powered technologies in daily life, such as chatbots, recommendation systems, and automation, and understanding their benefits and limitations.

Terminology: Familiarity with essential AI/ML terms, definitions, and commonly used concepts to build a strong foundational understanding.

Problem-Solving: The ability to select appropriate AI/ML approaches for basic challenges and recognize common issues faced in AI implementations.

AI/ML Workflow: Understanding the general process of building AI models, from data collection to model evaluation and deployment in simple terms.

Cloud Computing for AI: Understanding cloud platforms for AI tasks, including data storage, model training, and deployment, leveraging cloud-based tools and services