[TOC]

# 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