Introduction to Machine Learning
Technology · Machine Learning
A hands-on introduction to supervised learning, model evaluation, and responsible deployment patterns.
Explain core supervised learning workflows.
Choose metrics that align to business or mission goals.
Avoid common overfitting and leakage pitfalls.
Communicate model trade-offs to non-technical stakeholders.
Requirements
Comfort with spreadsheets or basic Python is useful.
Suitable for analysts and product teams.
No advanced mathematics required.
Section 1
Core concepts
What machine learning actually is
Video · 16m
Data splits and baselines
Reading · 14m
Evaluation metrics in context
Video · 19m
Section 2
From notebooks to decisions
Feature engineering basics
Video · 21m
Explaining model decisions
Project · 29m
Validation quiz
Quiz · 11m
Hazel Tan
Analytics Manager
Very approachable without being superficial. I now feel confident reviewing model assumptions with our data team.
Rohit Menon
Product Analyst
The evaluation sections are especially strong. They cut through a lot of confusion around model success metrics.
Machine learning foundations for practitioners
A preview lecture and curriculum walkthrough to help you judge fit before you enrol.
Senior Lecturer in Applied Machine Learning
Priya teaches machine learning foundations, explainability, and model operations for cross-functional teams.
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6
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4.7
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