Level 7 Microsoft Azure AI Apprenticeship

“The most successful organisations will be the ones that transform both technically and culturally, equipping their people with the skills and knowledge to become the best competitive asset they have. Human ingenuity is what will make the difference – AI technology alone will not be enough. At Microsoft, we’re on this journey just like everyone else, not least because the best learners make the best teachers. The larger point though, is not to be intimidated by the technology. Instead, get excited, develop your curiosity and let’s keep learning from one another.”

Simon Lambert, Chief Learning Officer for Microsoft UK

“The recent pandemic is significantly accelerating the demand for digital skills. Meeting this demand cannot just be a top-down process pushed by business leaders, it requires an enormous bottom-up effort from individuals at all levels who are self-motivated to improve their digital and AI-augmented skills."

Lord Clement Jones, former Chairman of the House of Lords Select Committee on Artificial Intelligence

"We are very excited to be partnering with Microsoft, a global leader in AI, to deliver this unique programme that will tackle the UK's AI skills shortage through apprenticeships"

Dr Raoul-Gabriel Urma, CEO of Cambridge Spark

• Tools for Data Science (eLearning) - Learn how to perform data analysis using Pandas and Numpy including filtering, aggregation, cleaning and applying imputation techniques.

• Data Science for business (eLearning) - Understand how data analysis fits into the wider data context for businesses, including key terms and connected workstreams.

• Maths for Data Science (eLearning)  - Understand the basics of maths for data science, including linear algebra, calculus and optimisation.

• Product Management for AI - develop a customer-centric product mindset to build products that solve their problems and serve their needs.

• Introduction to Machine Learning - Learn the foundations of machine learning and how to prepare data for training models.

• Supervised Learning - Learn about model selection and evaluation, including algorithms such as Decision Trees.

• Unsupervised Learning - Understand a wide range of unsupervised learning models and techniques to reveal latent structure within your data and covers topics including KMeans, hierarchical clustering, DBSCAN, PCA and t-SNE.

• Time Series Analysis - Build a more advanced understanding of tools and testing techniques for working with time series data with Python, Pandas, Numpy, the Prophet library as well as autoregressive models.

• Data, Privacy, Ethics & Regulations (eLearning) - Learn how to interpret policies, ethics and regulations in relation to AI and data.

• Ensemble Methods - Learn the fundamental principles behind ensemble techniques including random forests, lightgbm and xgboost and how these models can be evaluated. Understand the theory behind Support Vector Machines (SVMs), and develop the skills needed to build and evaluate them.

• Hackathon - Reinforce and develop the technical knowledge, practical skills and data science behaviours with a halfway hackathon.

• Pragmatic techniques for Model Evaluation - Learn how to optimise models for dealing with bias/variance as well as additional evaluation metrics, regularisation techniques and practical tips.

• Neural Networks and Deep Learning - Understand how neural networks are designed and how they operate, including different neural network architectures including CNNs and RNNs.

• Model Explainability & Interpretability - Become familiar with techniques for interpreting and explaining a range of machine learning models and deep neural networks.

• Preparation for End Point Assessment - Plan and review the project to be completed and presented as part of EPA, including presentation skills, report writing and mock technical test.