# 19: Course Summary

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__Summary of course topics__
**Supervised learning** - labeled data
- Linear regression
- Logistic regression
- Neural networks
- Support vector machines

**Unsupervised learning** - unlabeled data
- K-means
- PCA
- Anomaly detection

**Special applications/topics**
- Recommender systems
- Large scale machine learning

**Advice on building machine learning systems**
- Bias and variance
- Regularization
- What to do next when developing a system
- Algorithm evaluation
- Learning curves
- Error analysis
- Ceiling analysis