XGBoost topics
12 short topics in Data Science and Machine Learning. Each one is a single focused exercise with a clear piece of evidence at the end.
12 topics, one exercise each.
Work through them in order, or jump to the one you need.
These are draft topic outlines (draft; instructor review pending). Full lessons follow after review; ask Ajay if you need a topic sooner.
- 01
Dataset and feature representation
Investigate dataset and feature representation using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 02
Training validation and test separation
Investigate training validation and test separation using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 03
Model or estimator configuration
Investigate model or estimator configuration using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 04
Loss or objective functions
Investigate loss or objective functions using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 05
Fit and inference lifecycle
Investigate fit and inference lifecycle using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 06
Hyperparameter selection
Investigate hyperparameter selection using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 07
Training reproducibility
Investigate training reproducibility using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 08
Evaluation metrics
Investigate evaluation metrics using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 09
Overfitting and regularization
Investigate overfitting and regularization using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 10
Model artifacts and serialization
Investigate model artifacts and serialization using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 11
Serving or application integration
Investigate serving or application integration using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 12
Experiment diagnostics and comparisons
Investigate experiment diagnostics and comparisons using a repeatable XGBoost experiment; compare results with a baseline and record limitations. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course →
Learn the same ideas beyond XGBoost.
These courses share topics with this one, so you can compare how each language or tool handles the same concept.
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