Free AI courses from the Turing Institute

If you are interested in learning more about different aspects of artificial intelligence and data science, the Alan Turing Institute’s resources could be a good place to start. They have a number of free courses that cover a range of topics. Some of the courses are suitable for any learners. However, others have prerequisites such as a background in basic maths, Python programming experience, or knowledge of machine learning concepts.

The full list of courses can be found here.

The links to the individual courses are below. We’ve included a brief summary of what each course covers, and for whom it is suitable.

• AI Fairness on Social Media. This course provides a comprehensive review of the techniques in fairness on natural language processing (NLP) and graph mining, with an aim to critically assess and mitigate biases in real world examples. It is suitable for all researchers in data mining, artificial intelligence and social science. The audiences are assumed to have basic knowledge of probability, linear algebra and machine learning, and knowledge of Python is required.
• Operationalising Ethics in AI – Intermediate. The aim of this course is to relate the concepts of ethical AI principles into the systems design process. It is accessible for businesses, organisations or people developing, evaluating, or distributing AI. It requires a basic understanding of AI and the existing regulatory framework.
• Operationalising Ethics in AI – Expert. Covering practical applications of applying ethical AI principles into the systems design process. This advances on the skills in the intermediate course.
• Introduction to Transparent Machine Learning. An introduction to the essentials on transparent machine learning for learners of diverse backgrounds to understand and apply transparent machine learning in real-world applications with confidence and trust. Learners should have a knowledge of basic maths and Python for machine learning.
• Standards at a glance. This course is suitable for anyone with an interest in standards.
• How data lies. Designed to provide practical, actionable support to data scientists who are making efforts to be responsible, while recognising why this can present challenges. It is suitable for Data Scientists who are actively looking to be responsible in their work.
• Assessing and mitigating bias and discrimination in AI: Beyond binary classification. A guide to evaluating and mitigating bias in AI systems, going beyond binary classification tasks. The course is designed for a technical audience, specifically data science and machine learning practitioners or researchers who are concerned about the fairness of their algorithms.
• Fairness and Responsibility in Human-AI interaction in medical settings. This course is designed for clinicians and the stakeholders of clinical settings who use AI for augmented clinical reasoning and decision making.
• Assessing and Mitigating Bias and Discrimination in AI. This course introduces and provides a guide to evaluating and addressing issues of bias and fairness in AI systems. The first part is suitable for all learners, whereas the second part requires background knowledge of Python and machine learning concepts.
• Data Visualisation and Visual Analytics. This is an introductory course on Data Visualization using Python, suitable for anyone with basic experience of Python programming.
• Mathematics of Machine Learning – Summer School. This programme will equip researchers with the required tools to fully engage with modern literature on the theoretical foundations of machine learning. It requires knowledge of probability theory and linear algebra.

Lucy Smith , Managing Editor for AIhub.

Lucy Smith , Managing Editor for AIhub.