Core AI
First 2027 scholarship call
AI Foundations
Understand the models, data and evaluation concepts behind modern Artificial Intelligence systems.
Developed under the new AI Act model · Continuous updates
Edition 2026
200 hours of applied learning
8 academic credits
25 lessons + 6 workshops
Digital campus
Verifiable certificate
550 USD one payment · or 2 × 275 USD (today and after 30 days)
What you'll learn
- Explain supervised, unsupervised and generative learning.
- Distinguish training, inference and evaluation.
- Review data quality and check whether a model generalises.
- Explain neural networks, transformers and embeddings without coding.
- Assess model outputs against explicit quality criteria: metrics, error analysis and human review.
- Build an evidence-based evaluation protocol and a capability dossier.
- Apply the IntellCore AI Act Method™: classify the risk of an AI use case in your field and document it under the AI Act 2026.
Program curriculum
01 — What a model is and how it learns
- What AI is and how it reached today's models
- What a model is
- Learning from data: supervised, unsupervised and generative
- Training, inference and evaluation
- The course case: four proposals and one rule
02 — Data and machine learning
- Data: what a model learns from
- Features and representations
- Generalisation: overfitting, splits and distribution shift
- Classic model families and when to choose each
03 — Neural networks and transformers
- Layers, neurons and activations
- Optimisation: loss, gradient and epochs
- Transformers and attention
- Large language models: pre-training, tuning and inference
04 — Embeddings and generative models
- Embeddings: meaning as position
- Semantic search and retrieval-augmented generation
- Generating text: probability, sampling and hallucinations
- Comparing model families for a task
05 — Evaluation: metrics, error and human review
- Classification metrics
- Metrics for regression, ranking and generation
- Error analysis
- Human review and models as evaluators
06 — Final project: capability and evaluation dossier
- An evidence-based evaluation protocol
- Documenting models and data: cards and traceability
- Evaluating after deployment: drift and monitoring
- The capability dossier: writing and defence
07 — IntellCore AI Act Method™ applied to your field
- The EU AI Act 2026: risk levels, prohibited practices and obligations
- Transparency, human oversight and documentation in your professional field
- Classifying and auditing a real AI use case step by step
- Your compliance file with the IntellCore AI Act Method™: from system records to the review plan
Practical application
- · Final project: a model capability and evaluation dossier for the four Albarén Seguros proposals.
Practical project: Final project: a model capability and evaluation dossier for the four Albarén Seguros proposals.
Who is this for?
- · Professionals starting in AI
- · Team leads who receive model proposals
- · Business, quality, risk or procurement staff who must assess AI systems
Requirements
- · No coding required
- · Basic spreadsheet skills
- · About 6 hours per week
Meet your instructor
RAG & Knowledge Systems
Chair, RAG & Knowledge Systems
Chunking, embeddings, hybrid retrieval, re-ranking and grounding answers in verifiable sources.
Teaches: Prompt Engineering · Generative AI
Edition 2026
What each week includes
Every week of the program follows the same structure.
- 01
Explainer video per lesson
- 02
Guided reading (6 h)
- 03
IntellCore Labs · ~90 min workshop with data
- 04
Verified real case
- 05
IntellCore Podcast
- 06
IntellCore Radar
- 07
Self-assessment
- 08
Practice and exam (70%, 3 attempts)
- 09
Extended practice (5 h)
- 10
IntellCore AI Act Method™ module applied to your field (30 h)
- 11
Applied final project (30 h)
- 12
6 modules × 23 h + IntellCore AI Act Method™ module 30 h + final assessment 2 h + final project 30 h = 200 h
- 13
Downloadable PDF summary
Verifiable certificate
200 hours · 8 academic credits
Unique verifiable code at intellcore.ai/verify
Student reviews
Reviews are published from verified students only, after the first cohort completes the program. We do not publish testimonials we cannot attribute.
