EXIN BCS Machine Learning Award - Including Exam

- IT Professionals
- Software Developers
- Data Analysts
- Data Scientists
- Business Leaders & AI Strategists
- Project Managers
- Product Managers
- Engineers & Technical Consultants
- Individuals with an interest in AI and a background in science, engineering, knowledge engineering, finance, education, or IT services

What will you learn?

• Gain a structured, easy-to-follow introduction to machine learning fundamentals, including supervised, unsupervised, and semi-supervised learning—even if you’re new to AI.
• Understand regression, classification, clustering, and deep learning—the core techniques behind AI-powered decision-making, automation, and predictive analytics.
• Learn how machines recognize patterns, train on data, and improve over time without needing a PhD in statistics.
• Explore Python, TensorFlow, Scikit-Learn, and R—the leading tools for building ML models, even if you have no prior coding experience.
• Learn how to collect, clean, preprocess, and transform data for machine learning—key skills needed to build accurate and reliable AI models.
• From Netflix-style recommendations and chatbots to fraud detection and cybersecurity, understand how machine learning is driving innovation across industries.
• Get a complete picture of how ML models are trained, tested, fine-tuned, and optimized for real-world deployment.
• Understand the biases, legal concerns, and ethical implications of machine learning to ensure responsible AI implementation.

Introduction to Machine Learning

•  Definition and Overview 
• Applications of Machine Learning
• Role of Learning Agents
• Concept of Deep Learning
• Purpose and Function of Neural Networks
• Integration with Knowledge-Based Systems
• Data Interaction in Machine Learning

Programming in Machine Learning

• Programming Languages for Machine Learning 
• Software Tools: Open Source vs. Proprietary 

 Machine Learning Algorithms

• Mathematical Foundations
• Common Algorithms in Machine Learning 
• Types of Learning: Supervised, Unsupervised, and Semi-Supervised 

Practical Applications of Machine Learning 

• Problem Identification for Machine Learning Solutions
• Data Preparation and Processing 
• Training Machine Learning Models 
• Testing and Validation of Models 
• Evaluation and Reporting of Results to Stakeholders

• No formal prerequisites are required; however, a basic understanding of IT and mathematics is recommended

Exam Details

Duration: 30 minutes
Number of Questions: 18 - of which 2 scenario-based questions worth 2 points each (Multiple Choice)
Pass mark: 65%
Open book: No
Electronic equipment allowed: No
Level: Foundation
Languages: English, Portuguese