What you'll build4 outcomes
- 01Model real problems using supervised and unsupervised techniques.
- 02Use NumPy and core Python tooling for ML workflows.
- 03Evaluate models with practical metrics and generalization checks.
- 04Complete mini projects and a capstone with measurable outcomes.
Full curriculum15 items · 5 projects
01Course Introduction
02Introduction to ML & the Language of Data
03Foundational Tools : Basics of NumPy
04Supervised Learning I : Linear Regression & Optimization
05Supervised Learning II : Classification & Decision Boundaries
06Mini Project 00 : Linear Regression
Project
07Supervised Learning III : Model Evaluation
08Regularization & Generalization Control
09Unsupervised Learning I : Clustering Algorithms
10Unsupervised Learning II : Dimensionality Reduction
11Mini Project 01 : Ensemble Machine Learning
Project
12Hands-on Introduction to Reinforcement Learning
13Mini Project 02 : Unsupervised Learning
Project
14Capstone Project Part 1 : Project Scoping & EDA
Project
15Capstone Project Part 2 : Modeling, Evaluation & Tuning
Project
Instructor
Isuru Alagiyawanna
Lead Instructor & Head of AI Engineering
Isuru leads Zuu Crew AI'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.
University of MoratuwaRobert Gordon University, UKHead of AI Eng, Veracity GroupAWS ML EngineerTensorFlow Certified