CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost 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 CatBoost.
These courses share topics with this one, so you can compare how each language or tool handles the same concept.
More in Data science and machine learning libraries.
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