This lesson is still being designed and assembled (Pre-Alpha version)
Toggle navigation
Home
Code of Conduct
Setup
Episodes
What is ML: A motivating Example
What is ML: Machine Learning vs Traditional Modelling
Coffee Break
What is ML: A Taxonomy of Machine Learning
What is ML: Why Use Machine Learning?
What is ML: When to use it
What is ML: How to use it
Data Pipelines: Getting and Viewing Data
Lunch Break
Data Pipelines: Data Munging
Data Pipelines: Data
Data Pipelines: Tidying Data
Data Pipelines: Feature Engineering
Data Pipelines: Data Augmentation
Data Pipelines: A Data Pipeline
Data Pipelines: Recipe
ML Models: Types of ML Algorithms
ML Models: Types of ML Problems
ML Models: Looking Inside the Black Box
ML Models: Training and Learning
ML Models: Recipe
Testing and Verification: Introduction
Testing and Verification: Metrics of Performance
Testing and Verification: Overfitting
Testing and Verification: Validation and Hyperparameter Tuning
Testing and Verification: Recipe
All in one page (Beta)
Extras
Reference
About
Lesson Design
Discussion
Figures
Instructor Notes
License
Improve this page
CSIRO Data School - Introduction to Machine Learning
: Instructor Notes
FIXME