Machine learning training for all experience levels
Machine learning has become cornerstone of modern tech, powering everything from personalised recommendations to advanced predictive analytics. As organisations begin to scale AI transformations, the ability to design, train and deploy machine learning solutions is now one of the most sought after skill sets for data teams.
To help you build the expertise needed to thrive in this fast‑evolving field, we’ve curated the leading machine learning courses available right now.
Machine learning adoption has become increasingly widespread in recent years, with 81% of Fortune 500 companies using machine learning in their customer service, supply chain and cybersecurity departments.
However significant skills gaps still remain, with many businesses lacking the talent and capability to scale and deploy successful AI projects. Explore the best courses and apprenticeships to help you develop machine learning capabilities and bridge the gap between ambition and impactful machine learning solutions.
AI Engineer Level 6 Apprenticeship
The AI Engineer Level 6 apprenticeship programme is designed to help organisations accelerate their AI adoption by developing in‑house specialists who can design, build, and deploy real-world Generative AI and Machine Learning solutions.
With organisations increasingly investing in AI, demand for professionals who can build, integrate and optimise AI systems has never been greater.
This programme benefits from funding through the Growth and Skills Levy, and supports in future-proofing your workforce for the next era of AI transformation.
Who's it for?
The programme is designed for software engineers, developers and technical professionals looking to build expertise in machine learning, generative AI and AI-powered application development.
AWS Machine Learning Engineer
The AWS Certified Machine Learning Engineer certification builds practical skills across the machine learning lifecycle, from data preparation and model development to deployment and monitoring. Learners gain hands-on experience with Amazon SageMaker and AWS machine learning services, helping them develop the expertise needed to build and operationalise machine learning solutions in the cloud.
Who's it for?
This certification is well suited to AI engineers, machine learning engineers, data scientists and cloud professionals looking to validate their technical skills and deepen their expertise in building machine learning solutions on AWS.
Machine Learning on Google Cloud
The introduction to machine learning on Google Cloud course provides a practical introduction to building both predictive and generative AI solutions on Google Cloud. Learners explore the tools and services used across the AI lifecycle, including Vertex AI, Gemini, AutoML and BigQuery ML, gaining an understanding of how AI projects are developed, deployed and managed in cloud environments.
Through hands-on exercises, participants develop skills in machine learning, generative AI and AI application development while building familiarity with Google's AI ecosystem.
Who's it for?
This course is designed for AI developers, data scientists and machine learning engineers looking to build their expertise on Google Cloud.
Machine Learning with R and Python
This course helps learners develop practical data science, machine learning and AI skills using R. Covering the full data science lifecycle, it explores exploratory analysis, visualisation, machine learning model development, deployment strategies and the governance considerations needed for successful AI projects. Through a combination of expert-led instruction and hands-on labs, learners gain a deeper understanding of how data science can be used to generate insights, solve business problems and create value from data.
Who's it for?
This course is ideal for data analysts, software developers, data engineers and other technical professionals looking to progress towards a career in data science.
Advanced Machine Learning with Databricks
Advanced ML with Databricks is designed for professionals looking to take their machine learning skills beyond model development and into production. Learners explore how to build scalable machine learning workflows using Databricks, with a focus on MLOps, lifecycle management, testing, deployment and model monitoring. It's particularly valuable for teams looking to operationalise machine learning and develop production-ready AI systems.
Who's it for?
This course is ideal for data scientists, machine learning engineers and MLOps professionals who want to scale and manage machine learning workloads using Databricks.
Upskill your team in machine learning
Machine learning is no longer a niche specialism, it's central to how modern organisations innovate and unlock new sources of value. It empowers business to act faster and make greater data-driven decisions.
We specialise in helping organisations build that capability at scale. Whether you’re upskilling existing teams, growing new AI talent pipelines, or embedding machine learning skills across the wider business, our courses and apprenticeships give your people everything they need to deliver real impact.
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Machine learning FAQs
What is machine learning?
Machine learning is the technology behind many of the AI tools and services we use every day. It enables computers to learn from data, identify patterns and make predictions without needing to be explicitly programmed for each task.
From personalised recommendations and fraud detection to forecasting and generative AI, machine learning is helping organisations make better decisions, automate processes and unlock greater value from their data.
What does a machine learning engineer do?
Machine learning engineers turn AI concepts into real-world solutions. They bridge the gap between data science and software engineering, taking models developed by data teams and building the systems needed to deploy, scale and maintain them in production.
Whether they're creating recommendation engines, automating business processes or supporting generative AI applications, machine learning engineers play a key role in helping organisations move from experimentation to implementation.
What skills does a machine learning engineer need?
A successful machine learning engineer needs a combination of programming, data and engineering skills. Understanding how machine learning models work is important, but so is knowing how to build reliable systems that can be deployed and maintained in real-world environments.
Many professionals develop expertise in areas such as Python, data analysis, cloud platforms, MLOps and model deployment, alongside a solid understanding of machine learning concepts and algorithms.
Which platforms should I learn for machine learning?
There is no single platform for machine learning, but gaining experience with the major cloud ecosystems can open up a wide range of career opportunities. Platforms such as Microsoft Azure, AWS and Google Cloud provide tools for building, training and deploying machine learning models at scale.
Many organisations also use platforms such as Databricks to manage data, streamline machine learning workflows and support AI development. The best choice often depends on your industry, role and career goals.
Which programming languages do I need for machine learning?
Python is the language most commonly associated with machine learning and AI, making it a great place to start. Its extensive libraries and strong community support have made it the industry standard for developing machine learning models.
Alongside Python, many machine learning professionals also use SQL for working with data and may encounter languages such as R, Scala or Java depending on their role, tools and technical environment.
What jobs are available in machine learning?
Machine learning skills can open the door to a wide range of technology careers. Roles such as Machine Learning Engineer, AI Engineer, Data Scientist and MLOps Engineer are all in high demand as organisations continue to invest in AI and data-driven innovation.
As machine learning becomes embedded across more business functions, these skills are also becoming increasingly valuable in areas such as cyber security, cloud computing, software development and product management, creating opportunities across a broad range of industries and specialisms.