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EXIN BCS Machine Learning Award - Including Exam

  • Kursuskode MACHL-AWARD
  • Varighed 1 Dag

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The 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

Kursusdato

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Målgruppe

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- 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

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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.

Kursusindhold

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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

Forudsætninger

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

Certificeringstest

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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

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