Professional Certificate in Applied AI & Machine Learning
This page is the single official record of this programme. Transcripts, syllabus PDFs, the learner workspace and the public verification page are all generated from it, and each printed syllabus carries the version and fingerprint shown below so evaluators can compare them line by line.
Each module is 60 hours: 20 h guided instruction, 20 h supervised lab, 12 h project studio, 8 h assessment. Programme version 2026.10 · fingerprint 32765A39D1FEA4BE
| Code | Course | Hours | ECTS |
|---|---|---|---|
| SWY-02-M01 | Mathematics for AI | 60 | 3 |
| SWY-02-M02 | Python for Data Science | 60 | 3 |
| SWY-02-M03 | Data Visualization & Storytelling | 60 | 3 |
| SWY-02-M04 | Supervised Learning | 60 | 3 |
| SWY-02-M05 | Unsupervised Learning & Clustering | 60 | 3 |
| SWY-02-M06 | Deep Learning Fundamentals | 60 | 3 |
| SWY-02-M07 | Computer Vision & CNNs | 60 | 3 |
| SWY-02-M08 | Natural Language Processing | 60 | 3 |
| SWY-02-M09 | Model Deployment & MLOps | 60 | 3 |
| SWY-02-M10 | Capstone — AI for a Real-World Problem | 60 | 3 |
M01 Mathematics for AI · Module 1 · 60 h
Linear algebra, calculus, probability, statistics for ML.
- • Linear algebra
- • calculus
- • probability
- • statistics for ML
M02 Python for Data Science · Module 2 · 60 h
NumPy, Pandas, cleaning, exploratory analysis.
- • NumPy
- • Pandas
- • cleaning
- • exploratory analysis
M03 Data Visualization & Storytelling · Module 3 · 60 h
Matplotlib, Seaborn, Tableau dashboards.
- • Matplotlib
- • Seaborn
- • Tableau dashboards
M04 Supervised Learning · Module 4 · 60 h
Regression, decision trees, random forests, SVM.
- • Regression
- • decision trees
- • random forests
- • SVM
M05 Unsupervised Learning & Clustering · Module 5 · 60 h
K-Means, hierarchical, PCA, anomaly detection.
- • K-Means
- • hierarchical
- • PCA
- • anomaly detection
M06 Deep Learning Fundamentals · Module 6 · 60 h
Neural nets, activation functions, backprop, TensorFlow / Keras.
- • Neural nets
- • activation functions
- • backprop
- • TensorFlow / Keras
M07 Computer Vision & CNNs · Module 7 · 60 h
Image processing, CNNs, object detection, face recognition.
- • Image processing
- • CNNs
- • object detection
- • face recognition
M08 Natural Language Processing · Module 8 · 60 h
Tokenization, sentiment analysis, Transformers, chatbots.
- • Tokenization
- • sentiment analysis
- • Transformers
- • chatbots
M09 Model Deployment & MLOps · Module 9 · 60 h
Serving APIs, monitoring drift, ethical AI.
- • Serving APIs
- • monitoring drift
- • ethical AI
M10 Capstone — AI for a Real-World Problem · Module 10 · 60 h
Deploy a predictive model with a usable interface.
- • Deploy a predictive model with a usable interface
- • Written examination — 30% of the course mark
- • Laboratory / practical portfolio — 40% of the course mark
- • Project defence — 20% of the course mark
- • Professional conduct & participation — 10% of the course mark