IBM AI Fundamentals
Build a solid foundation in AI concepts, including machine learning, deep learning, generative AI, and responsible AI practices.
Exam Details
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Step-by-Step RoadmapEstimated total: 2 to 3 weeks
What is AI? Core Concepts
Understand artificial intelligence, machine learning, deep learning, and how they relate to each other.
What to Learn
- AI vs machine learning vs deep learning
- Supervised, unsupervised, and reinforcement learning
- Neural networks and how they learn
- Natural language processing basics
- Computer vision basics
Resources
- IBM SkillsBuild: AI Fundamentals badge (free)
- Coursera: IBM AI Foundations for Business Specialisation
Think of it as nested layers: AI is the broadest concept, ML is a subset of AI, deep learning is a subset of ML. The exam tests whether you can correctly categorise a given technology into the right layer.
Machine Learning in Practice
Learn how ML models are built, trained, and evaluated in real-world scenarios.
What to Learn
- Training data and feature engineering
- Model training and testing
- Overfitting and underfitting
- Classification vs regression
- Clustering algorithms
Resources
- IBM Watson Studio (free lite tier)
- IBM SkillsBuild Machine Learning module
IBM loves IBM Watson product examples. Know what Watson Studio, Watson Assistant, and Watson NLP do so you can recognise them in scenario questions.
Generative AI and Foundation Models
Understand foundation models, large language models, and IBM's watsonx platform.
What to Learn
- Foundation models (pre-trained, adaptable)
- Large language models and their training
- IBM watsonx.ai platform
- Prompt engineering for LLMs
- AI model governance and explainability
Resources
- IBM watsonx documentation
- IBM SkillsBuild: Generative AI module
IBM's watsonx is their enterprise AI platform. Knowing the three components (watsonx.ai for building, watsonx.data for data, watsonx.governance for trust) covers many exam questions.
AI Ethics and Responsible AI
Understand AI bias, fairness, transparency, and IBM's principles for trusted AI.
What to Learn
- AI bias and fairness
- Explainability (why did the model decide this?)
- IBM's AI ethics pillars: explainability, fairness, robustness, transparency, privacy
- AI governance and regulation
- Human-centred AI design
Resources
- IBM AI Ethics documentation
- IBM SkillsBuild: Ethics in AI module
IBM's five pillars of trusted AI: explainability, fairness, robustness, transparency, privacy. These appear on the exam. Connect each pillar to a real-world problem it solves (e.g. fairness prevents discriminatory loan approvals).
Complete Badges and Projects
Finish all modules, earn IBM digital badges, and build a portfolio project.
What to Learn
- Complete all IBM SkillsBuild modules
- Earn IBM AI Fundamentals digital badge
- Share on LinkedIn and Credly
- Optional: build an IBM Watson demo
Resources
- IBM SkillsBuild: ibm.com/training/skillsbuild
- Credly for digital badges
IBM issues Credly digital badges that can be displayed on LinkedIn. They are recognised by employers and show verifiable proof of completion. Finish all modules before the assessment as earlier modules feed into later ones.