Applied Machine Learning

University-grade applied ML: regression, classification, model evaluation and deployment thinking, taught by NUS faculty over five Saturdays. Suited to analysts and engineers formalising ML skills.

What you'll walk away with

• ✓ Core ML algorithms and when to use them
• ✓ Model evaluation and avoiding overfitting
• ✓ A graded capstone with NUS certificate

Curriculum, module by module

1. Block 1 — ML foundations

   Supervised learning framing, train/test discipline, the bias-variance trade-off in practice. 7 hrs.

2. Block 2 — Core algorithms

   Regression, classification, trees and ensembles — with scikit-learn labs. 10 hrs.

3. Block 3 — Model evaluation

   Metrics beyond accuracy, cross-validation, overfitting diagnostics. 7 hrs.

4. Block 4 — Deployment thinking

   From notebook to pipeline: feature stores, monitoring, drift. 4 hrs.

5. Capstone

   Graded end-to-end project with NUS faculty review. 7 hrs.

Who this course is for

• → Analysts and engineers formalising ML skills with a university credential
• → Professionals targeting data-science-adjacent roles
• → Technical leads who must evaluate ML proposals credibly

Materials you keep

• 📎 NUS courseware and lab notebooks
• 📎 Graded capstone with faculty feedback
• 📎 NUS PACE certificate (SB-Accredited)

Duration & format

5 days (35 hours), in-person delivery.