Guided path
Machine Learning
Learn how machine-learning algorithms fit, generalise and fail, then practise validation, tuning, calibration, interpretation and production thinking.
52 core steps95 detailed lessons13 modules
01
02
03
04
05
Module 5 · 7 detailed lessons
Instance & Kernel Methods
1
Browse all 7 lessons in this module →KNN ClassificationPython · Classification
Open →2Distance MetricsPython · Workflow
Open →3Kernel Trick IntuitionPython · Workflow
Open →4Scaling RequirementsPython · Scaling
Open →06
07
08
Module 8 · 6 detailed lessons
Unsupervised Learning
1
Browse all 6 lessons in this module →K MeansPython · Workflow
Open →2DBSCANPython · Pca
Open →3PCAPython · Pca
Open →4T Sne and Umap UsagePython · Workflow
Open →09
Module 9 · 8 detailed lessons
Validation & Leakage
1
Browse all 8 lessons in this module →HoldoutPython · Workflow
Open →2Stratified K FoldPython · Workflow
Open →3Nested CvPython · Split
Open →4Repeated EvaluationPython · Workflow
Open →10
Module 10 · 8 detailed lessons
Hyperparameter Optimisation
1
Browse all 8 lessons in this module →Search Space DesignPython · Workflow
Open →2Random SearchPython · Hpo
Open →3Successive HalvingPython · Hpo
Open →4Multi Metric TuningPython · Workflow
Open →11
Module 11 · 9 detailed lessons
Metrics, Calibration & Thresholds
1
Browse all 9 lessons in this module →Confusion MatrixPython · Workflow
Open →2ROC AUCPython · Classification
Open →3Regression MetricsPython · Regression
Open →4Threshold OptimisationPython · Classification
Open →12
Module 12 · 8 detailed lessons
Interpretation & Production Thinking
1
Browse all 8 lessons in this module →Permutation ImportancePython · Workflow
Open →2SHAP IntuitionPython · Workflow
Open →3Batch vs API InferencePython · Workflow
Open →4Retraining GovernancePython · Workflow
Open →13