Introduction
Avoiding machine learning mistakes
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Using the exercise files
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1. Mistakes to Avoid
Assuming data is good to go
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Neglecting to consult subject matter experts
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Overfitting your models
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Not standardizing your data
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Focusing on the wrong factors
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Data leakage
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Forgetting traditional statistics tools
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Assuming deployment is a breeze
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Assuming machine learning is the answer
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Developing in a silo
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Not treating for imbalanced sampling
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Interpreting your coefficients without properly treating for multicollinearity
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Evaluating by accuracy alone
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Giving overly technical presentations
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Conclusion
Take your machine learning skills to the next level
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Ex_Files_Mistakes_ML.zip
(30 KB)