This chapter introduced you to two fundamental supervised machine learning algorithms: the Naive Bayes algorithm and linear support vector machines. More specifically, you learned about the following topics:
- How the Bayes theorem is used to produce a probability, to indicate whether a data point belongs to a particular class or category
- Implementing the Naive Bayes classifier in scikit-learn
- How the linear support vector machines work under the hood
- Implementing the linear support vector machines in scikit-learn
- Optimizing the inverse regularization strength, both graphically and by using the GridSearchCV algorithm
- How to scale your data for a potential improvement in performance
In the next chapter, you will learn about the other type of supervised machine learning algorithm, which is used to predict numeric values, rather than classes and categories: linear regression...
