External Learning Courses

Elements of AI

Beginner Level Course

Provider: University of Helsinki & MinnaLearn
Time commitment: ~ 5-10 hours per week over 6 weeks

Join over 1.8 million learners worldwide in this two-part series designed to make AI accessible to everyone. Part 1 introduces AI concepts with no math or programming required, while Part 2 (Building AI) covers the actual algorithms behind AI methods for those with basic Python skills. Both courses combine theory with practical exercises and can be completed at your own pace.

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AI For Everyone

Beginner Level Course

Provider: Coursera – Andrew Ng
Time commitment: ~ 6 hours

A non-technical course designed specifically for business leaders and professionals who want their organisations to leverage AI effectively. Learn what AI can and cannot do, understand AI terminology, identify AI opportunities in your organisation, and navigate the AI transformation process. With over 50,000 reviews, this course provides the foundational knowledge needed to have informed conversations about AI strategy and implementation.

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Ethics of AI

Beginner Level Course

Provider: University of Helsinki & Mooc)
Time commitment: ~ 50-60 hours (including assignments)

Explore the ethical dimensions of artificial intelligence in this comprehensive free course. Covering topics from non-maleficence and accountability to transparency, human rights, and fairness, this self-paced program helps learners develop critical thinking skills around AI ethics. Perfect for anyone interested in understanding what AI ethics means and how to approach AI development in an ethically sustainable way. No technical background required.

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Generative AI Explained

Beginner Level Course

Provider: NVIDIA
Time commitment: ~ 2 hours

A beginner-friendly introduction to the fundamentals of generative AI. This no-code course demystifies how generative AI works, explores real-world applications across industries, and examines the challenges and opportunities in this rapidly evolving field. Ideal for non-technical professionals looking to understand the concepts behind AI-powered content generation.

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Generative AI: Prompt Engineering Basics

Beginner Level Course

Provider: Coursera - IBM)
Time commitment: ~ 9 hours

Master the art of communicating effectively with AI models through strategic prompt design. This hands-on course teaches essential techniques like zero-shot and few-shot prompting, along with advanced approaches such as Chain-of-Thought and Interview Pattern methods. Features practical labs using IBM Watsonx and other industry tools, making it ideal for professionals seeking to maximise their productivity with generative AI tools like ChatGPT.

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Introduction to AI with Python

Intermediate Level Course

Provider: Harvard University
Time commitment: ~7 weeks (10-30 hours per week)

Dive deep into the algorithms and concepts that power modern AI in this rigorous course from Harvard. Covering graph search algorithms, classification, optimisation, reinforcement learning, machine learning, and large language models, students work through hands-on Python projects to gain practical experience with AI libraries and principles. Requires CS50x or at least one year of Python experience. Ideal for learners ready to build intelligent systems from the ground up with a strong theoretical foundation.

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Machine Learning with Crash Course

Intermediate Level Course

Provider: Google
Time commitment: ~ 15 hours

Google's comprehensive 15-hour self-study course covering fundamental machine learning concepts with hands-on exercises. Recently updated to include generative AI topics, large language models, and AutoML. Features interactive visualisations, video explainers, and real-world programming exercises. From data preparation and neural network architectures to production ML systems and responsible AI practices, this free course provides both breadth and depth. Earn badges for completing modules—perfect for anyone building practical ML skills with industry-standard approaches.

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ChatGPT Prompt Engineering for Developers

Intermediate Level Course

Provider: DeepLearning.AI – Coursera Guided
Time commitment: ~ 2 hours

A practical 2-hour guided project teaching developers how to harness the power of large language models through the OpenAI API. Learn the two key principles for writing effective prompts, systematically engineer optimal prompts for real-world tasks like summarising, inferring, transforming text, and build a custom chatbot. Includes hands-on exercises in Jupyter notebooks. Requires only basic Python knowledge, making it accessible for beginners while remaining valuable for advanced ML engineers seeking cutting-edge prompt engineering techniques.

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Intro to Data Analysis

Intermediate Level Course

Provider: Udacity
Time commitment: ~ 20 hours

Master the complete data analysis process from posing questions to communicating findings in this free Python-focused course. Learn to wrangle data, draw conclusions, and make predictions using essential libraries including NumPy, Pandas, and Matplotlib. Progress from pure Python analysis to efficient vectorised operations with professional-grade tools. Requires comfort with Python programming concepts. An excellent foundation course that prepares learners to understand how data wrangling, exploration, and analysis phases interconnect in real-world projects.

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Hands-on Courses for ML Engineers

Advanced Level Course

Provider: Google
Time commitment: ranging from 1 - 24 hours

A comprehensive, hands-on learning path designed for experienced practitioners ready to design, build, and productionise ML systems at scale. This curated collection of on-demand courses and labs guides you through the complete ML engineering workflow using Google Cloud technologies: from managing large, complex datasets and creating repeatable code to deploying generative AI solutions and implementing MLOps practices.

You'll gain practical experience with Vertex AI, TensorFlow, BigQuery ML, feature engineering, model architecture design, and ML pipeline creation. The path covers critical production considerations including data governance, hyperparameter tuning, batch and online predictions, and model monitoring. Ideal for data engineers and developers with strong programming skills (particularly Python) who want to advance into production ML engineering roles.

Upon completion, you'll be well-prepared to pursue the industry-recognised Google Cloud Professional Machine Learning Engineer certification. Requires intermediate to advanced technical background with 3+ years of hands-on experience recommended for certification.

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