Volume 6 — Machine Learning¶
Models that generalise
Supervised and unsupervised learning built up from linear regression, with the parts that actually decide whether a model works: features, validation, leakage and evaluation.
Outlined, not yet written
This volume is fully outlined but the lessons are not yet authored. The chapter list below is the plan of record, not a promise that content exists.
What you will be able to do¶
- Frame a business problem as a learning problem — or correctly decline to
- Build a leak-free training pipeline
- Choose metrics that match the decision being made
- Explain a model’s behaviour to a non-technical stakeholder
Chapters¶
- What machine learning is and is not
- Features, labels, training and inference
- Linear and logistic regression
- Decision trees
- Random forests and gradient boosting
- Clustering and dimensionality reduction
- Feature engineering
- Train/test/validation and cross-validation
- Leakage: the bug that flatters you
- Evaluation metrics and choosing the right one
- Imbalanced data
- Model interpretability