Four Rare Machine Learning Skills All Data Scientists Need-Course introduction
Course overview
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The Machine Learning Glossary (optional)
Four Rare Machine Learning Skills All Data Scientists Need-Uplift modeling
Uplift modeling I: optimize for influence and persuade by the numbers
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Uplift modeling II: modeling over treatment and control groups
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Uplift modeling III: how it works – for banks and for Obama
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Uplift modeling IV: improving churn modeling, plus other applications
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Complementary readings on uplift modeling (optional)
Four Rare Machine Learning Skills All Data Scientists Need-The accuracy fallacy
Accuracy fallacy: orchestrating the media's bogus coverage of ML
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More accuracy fallacies: predicting psychosis, criminality, & bestsellers
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Complementary reading related to the accuracy fallacy (optional)
Four Rare Machine Learning Skills All Data Scientists Need-P-hacking
P-hacking: a treacherous pitfall
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P-hacking: your predictive insights may be bogus
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P-hacking: how to ensure sound discoveries
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Complementary materials on p-hacking (optional)
Four Rare Machine Learning Skills All Data Scientists Need-The paradox of ensemble models
Ensemble models and the Netflix Prize
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Supercharging prediction: ensembles & the generalization paradox
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The generalization paradox of ensembles (optional)
DEMO - Training an ensemble model (optional)
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Four Rare Machine Learning Skills All Data Scientists Need-Course review
Course conclusions
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Further learning options