Foundations of Machine Learning

What You Will Learn

• Model real problems using supervised and unsupervised techniques.
• Use NumPy and core Python tooling for ML workflows.
• Evaluate models with practical metrics and generalization checks.
• Complete mini projects and a capstone with measurable outcomes.

Full Curriculum

1. 01Course Introduction
2. 02Introduction to ML & the Language of Data
3. 03Foundational Tools - Basics of NumPy
4. 04Supervised Learning I - Linear Regression & Optimization
5. 05Supervised Learning II - Classification & Decision Boundaries
6. 06Mini Project 00 - Linear RegressionProject
7. 07Supervised Learning III - Model Evaluation
8. 08Regularization & Generalization Control
9. 09Unsupervised Learning I - Clustering Algorithms
10. 10Unsupervised Learning II - Understanding Dimensionality Reduction
11. 11Mini Project 01 - Ensemble Machine LearningProject
12. 12Hands-on Introduction to Reinforcement Learning
13. 13Mini Project 02 - Unsupervised LearningProject
14. 14Capstone Project Part 1 - Project Scoping & EDAProject
15. 15Capstone Project Part 2 - Modeling, Evaluation & TuningProject

Instructor

Isuru Alagiyawanna

Lead Instructor & Head of AI Engineering

Isuru leads Zuu Crew's technical direction and curriculum design. He has trained students and professionals across practical machine learning, MLOps, and AI engineering systems with a strong emphasis on shipping production-grade work.