Skip to main content

AI & Machine Learning Fellowship

Multiverse AI & Machine Learning Fellowship: curriculum, skills, aligned to Level 6 Machine Learning Engineer apprenticeship.

Updated over a week ago

AI & Machine Learning Fellowship Programme Overview

This programme empowers data science teams with the skills to transform raw data into valuable business intelligence. By integrating AI and machine learning, it enables organisations to shift to proactive decision-making, enhance efficiency by reducing manual processes and human error, and identify new business opportunities. It is part of our AI Academy Programmes.

Key Skills Gained:

  • Python for ML

  • Data science

  • Machine learning

  • Feature engineering and selection

  • Model development and training

  • Ethical and responsible AI

  • Data governance

  • Data change management

Business Outcomes:

  • Extract better business intelligence: Equip your team with advanced skills to extract meaningful insights, transforming raw data into valuable business intelligence.

  • Improve efficiency: Optimise your ML operations to significantly improve operational efficiency and productivity.

  • Discover deeper insights: Develop predictive models and adaptive systems, shifting your organisation from reactive to proactive decision-making.

Apprenticeship Qualification Achieved: Level 6 Machine Learning Engineer Duration: 15 month delivery, plus 3 month assessment


AI & Machine Learning Fellowship Indicative Curriculum Breakdown

Foundations: harnessing AI and machine learning

  • Month 1: Fundamentals of machine learning and AI

  • Month 2: Solving business problems with machine learning

  • Month 3: Data ethics and responsible AI

  • Month 4: Data preparation and feature engineering

  • Month 5: Hackathon: planning and preparing an AI solution

Application: building AI models with security best practices

  • Month 6: Model engineering and training

  • Month 7: Model evaluation

  • Month 8: Data security, privacy, and governance

  • Month 9: Hackathon: engineering an AI solution

Deployment: monitoring and maintaining models

  • Month 10: Model deployment

  • Month 11: Monitoring, maintenance, continuous learning

  • Month 12: Stakeholder communication

  • Month 13: Hackathon: deploying an AI solution

Note: This is an example curriculum, and specific details may vary per cohort.


AI & Machine Learning Fellowship Indicative Delivery Model

Monthly delivery model, approx. 26 hours per month total commitment. The exact time commitment will be outlined in the training plan that apprentices will receive at the start of their apprenticeship.

  • Structured Learning (~40% - 10 hours/month):

    • Asynchronous learning (3.5 hours): Online, self-paced content that sets the foundation of skills for the module.

    • Live workshops (3.5 hours): Instructor-led, interactive learning that dives deeper and reinforces the asynchronous content.

    • Group coaching (1 hour): Peer support and feedback on project deliverables.

    • Coach support (2 hours): Includes tutoring, progress reviews, & other individual/group support.

  • Working in Existing Role (~60% - 16 hours/month):

    • Project and applied learning (16 hours): Structured and unstructured application of learning to apprentices’ roles.


Did this answer your question?