Algorithms, AI & Machine Learning

Gain technical understanding of AI systems for auditing and governance

Formation

Disponible à tout moment

Certificat

E-learning

21 heures

Réalisable en ligne

Référence de la formation: FR-GL-AI-Babl-04

This technical crash course provides consultants, risk professionals, and policy experts with essential knowledge of modern AI and machine learning technologies. The course focuses on giving non-technical professionals the confidence to understand, assess, and audit automated decision systems, generative AI, and algorithmic models for risk analysis and governance purposes

This course is part of BABL AI's AI & Algorithm Auditor Certification Program, which builds core skills for AI auditing, governance, and regulatory compliance.

Les objectifs

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Build a solid technical foundation for evaluating AI systems
Learn key concepts of machine learning without deep coding knowledge
Improve your ability to support AI auditing, governance, and risk analysis

Understand the architecture and development process behind modern AI models

Le public ciblé

This course is ideal for:

Consultants and risk professionals in AI governance
Policy analysts and compliance specialists
AI governance and assurance professionals
Non-technical professionals involved in AI risk assessment

Les prérequis

No coding experience required (basic familiarity with Python helpful)
Basic understanding of statistics and mathematics (probability, linear algebra) beneficial
English proficiency required
Computer science knowledge helpful but not required

This course is designed to make complex AI topics accessible to professionals from non-technical backgrounds.

Le contenu de la formation

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The training covers:

Foundations of AI & ML

Overview of key machine learning types: supervised, unsupervised, reinforcement learning
AI/ML applications in real-world domains (e.g., hiring, healthcare, recommender systems)

Building Blocks of AI Models

Deep learning architectures: DNNs, CNNs, RNNs, Transformers
Key concepts: gradient descent, model layers, encoders, attention mechanisms
Introduction to large language models (LLMs) and tools like TensorFlow, PyTorch

Key Decision Points in AI Development

Data collection and dataset selection
Model validation and performance assessment

Introduction to Python for AI Governance

Basic elements of Python code in machine learning
Understanding Python’s role in AI auditing and governance

Informations importantes

This course is part of BABL AI’s AI & Algorithm Auditor Certification Program, which provides a complete foundation in AI auditing, risk management, and compliance.

Approximative duration 3 days and comprises 17 learning units.

Recommended equipment for accessing the Training Area :

To get the most out of your online training, make sure you have the following equipment:

Computer: PC or Mac
Internet connection: stable and good quality
Web browser: recent version of Chrome, Firefox, Edge or Safari
Office suite: OpenOffice, Microsoft Office or equivalent (spreadsheet, word processing, presentation, etc.)
PDF reader: To view your training materials
Storage: File storage capacity (hard drive, cloud, etc.)

You can contact the TÜV Rheinland team for assistance in setting up your training course.

Tip: Make sure your hardware and software are up to date for an optimal experience!

If you have a disability, please contact us before registering by sending an email to formation@fr.tuv.com. We will do everything we can to meet your training needs.

FAQ Suggestions

1. Do I need coding experience to take this course?
No. The course is designed for non-technical professionals. Basic familiarity with Python can be helpful but is not required.

2. Will I learn how to build AI models?
No. The focus is on understanding, assessing, and auditing existing AI systems, not developing new models.

3. How does this course support AI governance?
The course provides foundational knowledge to help you evaluate AI models, understand risk factors, and support responsible AI use within organizations.

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