EXIN BCS Machine Learning Award - Including Exam
- Course Code MACHL-AWARD
- Duration 1 day
Course Delivery
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Course Delivery
This course is available in the following formats:
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Company Event
Event at company
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Public Classroom
Traditional Classroom Learning
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopThe EXIN BCS Machine Learning Award gives you a clear, structured introduction to machine learning—covering key algorithms, data processing, model training, and real-world applications. You’ll learn how to prepare and transform data, understand supervised and unsupervised learning, and get hands-on insights into programming languages and ML frameworks such as Python, TensorFlow, and Scikit-Learn—even if you’re new to AI.
Updated 14/05/2026
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
Course Schedule
TopTarget Audience
Top- 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
Course Objectives
TopWhat 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.
Course Content
TopIntroduction 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
Course Prerequisites
Top- No formal prerequisites are required; however, a basic understanding of IT and mathematics is recommended
Test Certification
TopExam 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