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3 courses matching “tabular”

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Abstract cover artwork for Gradient Boosting in Depth: XGBoost and LightGBM, a Machine Learning course Machine Learning
Machine Learning intermediate

Gradient Boosting in Depth: XGBoost and LightGBM

Still the strongest default for tabular problems, and still widely misused. How the boosting objective actually works, which hyperparameters matter and which are noise, categorical handling, and reading SHAP values without over-reading them.

HT Hiroshi Tanaka 4.8

$75.00

11 hours

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Abstract cover artwork for Machine Learning Foundations with scikit-learn, a Machine Learning course Machine Learning
Machine Learning beginner

Machine Learning Foundations with scikit-learn

The honest introduction: how to frame a problem, build a baseline, and know when your model is fooling you. Linear and tree-based models, proper cross-validation, leakage detection, class imbalance, and calibration. Every concept is implemented on real tabular data rather than a toy dataset.

SD Sofia Duarte 4.8

$49.00

18 hours

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Abstract cover artwork for Feature Engineering That Actually Moves Metrics, a Machine Learning course Machine Learning
Machine Learning intermediate

Feature Engineering That Actually Moves Metrics

Most model gains come from features, not architecture. Target encoding without leakage, temporal features that respect causality, aggregation windows, interaction discovery and feature stores. Includes a rigorous ablation methodology so you can prove which features earned their place.

SD Sofia Duarte 4.7

$69.00

11 hours

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