Common Mistakes in Machine Learning
Master the art of avoiding critical errors that can derail your ML projects. Learn to identify and prevent common statistical biases, validation errors, and data leakage issues that plague machine learning implementations.
- Level: intermediate
- Duration: 8 weeks
- 9 sections
- Price: Free
Topics: Machine Learning, Statistical Bias, Model Validation, Data Science, Overfitting, P-Hacking
What's inside
- Common Mistakes in Machine Learning
- Omitted Variable Bias
- Selection Bias
- Confirmation Bias
- Measurement Error / Errors-in-Variables
- P-Hacking (Data Dredging)
- Spurious Correlations
- Confusion in Conditional Probability
- Confusing Correlation with Causation