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EXIN BCS Machine Learning Award

EXIN BCS Machine Learning Award

  • Overview

  • Why this Certification?

  • Who is this certification for?

The EXIN BCS Machine Learning Award is designed for individuals wishing to gain an understanding of the principles of machine learning and the process through which it can be developed.

The term ‘machine learning’ has increased in popularity in the last decade and is a technology which is becoming more commonly used within many organizations. With its ability to help solve business problems and develop new customer experiences, there is now a greater demand for individuals with the knowledge and skills to support organizations to successfully implement the technology to deliver improvements.

This award explores what machine learning is and how it is used in practice. It provides an introduction into the different types of machine learning and the tools and techniques required to develop it, including a basic introduction to algorithms. This award will enable candidates to understand these concepts at a foundation level, enabling them to be better informed and equipping them with knowledge which they can build upon through further study and application.

  • 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

Course Outline

  1. What is machine learning? 
    1. Define machine learning 
    2. Explain different applications of machine learning 
    3. Describe the role of a learning agent 
    4. Explain the concept of deep learning 
    5. Describe the purpose of a neural network 
    6. Illustrate how machine learning compliments knowledge-based systems 
    7. Explain the process through which machine learning works with data 
  2. Coding for machine learning 
    1. Explain the use of at least one coding language used in machine learning 
    2. Identify common open source and proprietary software used in coding for machine learning 
  3. Algorithms used in machine learning 
    1. Explain the use of mathematics in enabling a machine to solve numerical problems 
    2. List and describe typical algorithms used in machine learning 
    3. Describe supervised, unsupervised and semi-supervised learning 
  4. Machine learning in practice 
    1. Describe a particular problem that can be addressed through the use of machine learning 
    2. Outline typical tasks required in the preparation of data for developing a particular application of machine learning 
    3. Explain the process of training a machine learning model 
    4. Explain the process of testing a machine learning model 
    5. Discuss how to evaluate the results of testing in order to identify the information to be shared with key stakeholders

Course Info

  • Language:English
  • Note:

    Exam: 30 minutes, 18 MCQ, passing mark 65%

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