There is simply not enough room in this book to cover all the machine learning algorithms! Instead, it’s better to focus on the most common ones.
In the remaining part of this chapter, we’ll take a look at those for the following:
- Supervised Learning : You can boil down the algorithms to two variations. One is classification, which divides the dataset into common labels. Examples of the algorithms include Naive Bayes Classifier and k-Nearest Neighbor (neural networks will be covered in Chapter 4). Next, there is regression, which finds continuous patterns in the data. For this, we’ll take a look at linear regression, ensemble modelling, and decision trees.
- Unsupervised Learning : In this category, we’ll look at clustering. For this, we’ll cover k-Means clustering .
Figure 3-3 shows a general framework for machine learning algorithms.
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