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For machine learning practitioners

Certified AI Ethics course

Detect bias. Build better systems. Shape ethical AI that serves everyone.

Aligned with the EU AI Act

  • Self-paced, 100% online

  • 10h/week recommended

  • 3-4 months to graduate

Created in partnership with

TNO
University College Dublin
Sciences Po
Eticas
Cortexter
Women in AI
Women4Cyber

Build responsible AI systems

Artificial intelligence is already shaping high-stakes decisions: who gets hired, who qualifies for a loan, who gets access to services. But when it’s not built responsibly, it can amplify inequality, limit opportunity, and erode trust.

This course is part of the DIVERSIFAIR project, an EU-backed initiative created to help professionals build ethical AI that’s fair, transparent, and accountable — not just technically accurate.

Is this AI Ethics course for you? Yes, if you’re:

  • A data professional with 1+ year of experience in a coding-based role (e.g. Data Scientist, Machine Learning Engineer, AI Engineer)

  • Looking to stay ahead of regulations like the EU AI Act

  • Ready to embed fairness and accountability into your daily work

What can you expect?

  • Self-paced learning and flexible schedule

    Learn when it works for you — no fixed hours, no pressure. Just consistent progress on your terms.

  • Practical projects based on real-world scenarios

    Create solutions to challenges you’ll actually face on the job.

  • Certificate that proves your skills in ethical AI development

    Show employers you can build AI that’s both effective and responsible.

  • Tools & languages you’ll use

    • Python
    • Fairlearn
    • AIF360
    • Pandas
    • scikit-learn
    • Jupyter Notebooks

Program outline

You’ll complete 3 main sprints — each packed with hands-on learning and insights from real-world case studies. They will take you from foundational knowledge to practical application, and then into a specialization that fits your role.

01Foundational Concepts of Ethics in AI

Get a clear introduction to the ethics of AI technologies. You’ll explore how fairness is defined, where ethical considerations in AI arise, and how to evaluate systems using metrics and an audit framework.

02Technical Approaches to Fairness

Learn how to reduce unfair outcomes using proven techniques. You’ll explore fairness interventions at the data, model, and prediction levels — and apply ethical frameworks to guide your work.

03Emerging Fairness Frontiers and Implementation Strategies

Understand how fairness applies in large-scale AI systems, including LLMs. You’ll learn how to integrate fairness across the ML lifecycle, manage organizational responsibilities, and align with AI governance practices.

04Specialization — Practical Fairness for Data Scientists

Apply fairness in your real-world workflows: model training, deployment, and monitoring. You’ll work through best practices and implementation challenges — with support from expert mentors.

Learn anytime, anywhere

This is a fully virtual program. Study from anywhere, on your schedule — with support from your peers and mentors when you need it.

Learner studying remotely on their own schedule

Learn from professionals

Our Senior Team Leads and mentors are experienced professionals who are 100% up-to-date with the industry trends and will provide you consultations, 1on1 project reviews, and feedback.

Turing College education team

Graduate in 4 months or less — you set the pace

This program is flexible by design. Most learners complete it in 3–4 months with a ~10 hour/week commitment. But if you need to move faster or slower, that’s totally up to you. You’re in control.

Graduation timeline — most learners graduate in 3 to 4 months
Graduation timeline — most learners graduate in 3 to 4 months
Graduation timeline — most learners graduate in 3 to 4 months

Our learners work at top companies

Wise
Nord Security
Vinted
Tesla
Telia
Danske Bank
Hostinger
Wix
Continental
Macaw
Barbora
Jio
Accenture
Siemens
Meta
Deutsche Bank
Western Union
Hitachi
Swedbank
Gjensidige
PwC
ING
Amazon
Guidehouse
Daimler Truck
Kiloverse

How admissions works

Our application process has two simple steps designed to help you and our team understand whether the program is the right next step for you.

  1. 1

    Application

    Tell us about your background and answer two short questions about your motivation and goals. Your application goes straight to our admissions team, who review it personally and get back to you within 24 hours on working days.
  2. 2

    Consultation

    If the program matches your goals, we’ll invite you to a 20-30 minute consultation. This is your chance to ask anything, understand exactly how the program works, check your financing options, and decide whether it’s the right fit for you.

Basic admission requirements:

Dedication of at least 10 focused hours per week

Focused hours are:

  • · Working on a computer (not a smartphone)
  • · Uninterrupted working time in a dedicated space
  • · Rested, not overworked (not after intense cognitive work)

Strong motivation

You need a clear and strong reason for joining this program. It should be your personal goal, and you should have specific reasons for wanting to improve your skills.

Experience in coding-based data role

You should have at least 1 year of experience working with code in a data context — for example, as a Data Scientist, ML Engineer, or AI Engineer.

Good English skills

You must demonstrate an English proficiency of at least B2 level to join the program.

Up-to-date computer

You’ll need a computer that can handle large datasets smoothly and runs an actively supported and updated operating system.

Join waiting list!

Our graduates have achieved life changing growth. You can too.

Kata Hernádi
Turing College has been a great experience. I loved the structure and content of the material. I had constant contact with instructors and peer learners. I find the peer and senior review concept really efficient.
Kata Hernádi
Delegation Team Coordinator @ Siemens Energy
I have used many various learning platforms, and Turing College is really different. Its learning model allows you to move at your pace while getting as much help from the community. I love that it doesn't just focus on your hard skills but soft skills as well.
Linda Oranya
Linda Oranya
Data Scientist @ Metasite Data Insights
After the course, I was able to get into work and solve real business problems easily.
Ovidijus Kuzminas
Ovidijus Kuzminas
ML engineer @ Oxus.AI
The sole fact that I joined Digital Marketing program by Turing College was a contributing factor to me getting a job.
Justas Sadauskas
Justas Sadauskas
Account Manager @ Defined Chase
I have finished two universities, and studied in most of the major online platforms to get extra certificates. And Turing College is one of the most advanced place so far.
Ignas Lukosevicius
Ignas Lukosevicius
Junior Media Buyer @ Pulsetto
Turing College is for those who want to master data science.
Edvard Sivickij
Edvard Sivickij
Data Analyst @ Kilo Health

FAQ

Why is it important to learn about AI ethics and governance now?

As the impact of AI grows across industries, so do the ethical challenges around how systems are designed and who they affect. Without clear ethical principles and guardrails, data can be misused and people can be harmed. This course helps you build the skills to address those challenges before they become risks.

How is this certification course different from others?

While other providers may focus on compliance or policy, Turing College’s course combines hands-on projects, expert feedback, and continuing professional development, giving you practical tools to shape AI practices ethically.
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Co-funded by the European Union

This project has received funding from the European Education and Culture Executive Agency (EACEA) in the framework of Erasmus+, EU solidarity Corps A.2 – Skills and Innovation under grant agreement 101107969.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Culture Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.