EXIN BCS Machine Learning Award

Machine learning is revolutionizing industries, powering fraud detection, personalized recommendations, and AI-driven automation—but how does it actually work?

The EXIN BCS Machine Learning Award is designed to give you a solid foundation in ML principles, coding, algorithms, and real-world applications—without requiring deep technical expertise.

Here’s why this certification is essential for you:

• Learn what machine learning is, how it works, and its role within AI.
• Get insights into neural networks, regression, classification, clustering, and deep learning, and understand how they solve real-world problems.
• Understand how to collect, preprocess, and transform data for machine learning models, ensuring better accuracy and performance.
• Get sweeping knowledge across recommendation engines (e.g. Netflix, Spotify) to object recognition, prediction,  and automation, and explore how ML is used in business globally.
• Become familiar with programming languages & ML frameworks such as Python, TensorFlow, Scikit-Learn, even if you have little programming experience.
• Learn how ML models are trained, tested, fine-tuned, and deployed in real-world scenarios.
• Understand the limitations, biases, and ethical considerations when implementing machine learning solutions.

• 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

• 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

The knowledge required for the exam is covered in the following literature:

 1. Aurélien Géron Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems O’Reilly (2022) ISBN: 978-1098125974 (hard copy)

2.  Oliver Theobald Machine Learning for Absolute Beginners: A Plain English Introduction Independently published (3 rd edition, 2021) ISBN: 979-8558098426

3. Gilbert Strang Linear Algebra and Learning from Data Wellesley-Cambridge Press (1 st edition, 2019) ISBN: 978-0692196380 (Hard copy)

4. Andrew Lowe, Steve Lawless Artificial Intelligence and Machine Learning Foundations: Learning From Experience BCS (2024) ISBN: 978-1780176734

5. Sarah Burnett AI in Business: Towards the Autonomous Enterprise BCS (2024) ISBN: 978-1780176673