Further reading
Check out the following resources for more information on the topics covered in this chapter:
- Deploying scikit-learn Models at Scale: https://towardsdatascience.com/deploying-scikit-learn-models-at-scale-f632f86477b8
- Local Outlier Factor for Anomaly Detection: https://towardsdatascience.com/local-outlier-factor-for-anomaly-detection-cc0c770d2ebe
- Model Persistence (from the scikit-learn user guide): https://scikit-learn.org/stable/modules/model_persistence.html
- Novelty and Outlier Detection (from the scikit-learn user guide): https://scikit-learn.org/stable/modules/outlier_detection.html
- Naive Bayes (from the scikit-learn user guide): https://scikit-learn.org/stable/modules/naive_bayes.html
- Outlier Detection with Isolation Forest: https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e
- Passive Aggressive Algorithm (video explanation): https://www.youtube.com/watch?v=uxGDwyPWNkU
- Python Context Managers and...
