Machine learning
Spootyfi learning pathway · Beginner to advanced
Ages 16+ · English · 10 suggested study hours · 3 levels
Build intuition for supervised and unsupervised learning, features, overfitting and model evaluation.
- Prepare training data
- Choose evaluation metrics
- Diagnose overfitting
Beginner · Lesson 1 of 3
Supervised learning
Supervised models learn from labelled examples, such as emails marked spam or not spam.
Practical activity
Define features and a label for a sample spam classifier.
Beginner · Lesson 2 of 3
Features and labels
Features are input variables; the label is the target to predict. Features should be available at prediction time.
Practical activity
Split 100 fictional examples into 60 training, 20 validation and 20 test.
Beginner · Lesson 3 of 3
Data splits
Training data fits the model, validation data supports tuning and test data estimates final performance.
Practical activity
Explain why a future outcome cannot be used as an input.
Reference desk
- Google Machine Learning Crash Course
- scikit-learn user guide
Original Spootyfi lessons and exercises; reference materials are not reproduced. Not affiliated with or endorsed by the reference providers. Course text is English; translations and expert editorial review are pending. Illustrations are conceptual.