10 Advice for Applying Machine Learning

10.1 Deciding What to Try Next

10.2 Evaluating a Hypothesis

10.3 Model Selection and Train/Validation/Test Sets

10.4 Diagnosing Bias vs. Variance

10.5 Regularization and Bias/Variance

10.6 Learning Curves

10.7 Deciding What to Do Next Revisited

11 Machine Learning System Design

11.1 Prioritizing What to Work On

11.2 Error Analysis

11.3 Error Metrics for Skewed Classes

11.4 Trading Off Precision and Recall

11.5 Data For Machine Learning