Pedro Domingos summarizes 12 key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.

• 1. Learning = Representation + Evaluation + Optimization
• 2. It’s Generalization that Counts
• 3. Data Alone Is Not Enough
• 4. Overfitting Has Many Faces
• 5. Intuition Fails in High Dimensions
• 6. Theoretical Guarantees Are Not What They Seem
• 7. Feature Engineering Is The Key
• 8. More Data Beats a Cleverer Algorithm
• 9. Learn Many Models, Not Just One
• 10. Simplicity Does Not Imply Accuracy
• 11. Representable Does Not Imply Learnable
• 12. Correlation Does Not Imply Causation