Types of machine learning models

While we have one example, there are many other examples, some of which we will touch on in the relevant chapters when we introduce each machine learning task.

However, we can broadly divide the preceding use cases and methods into two categories of machine learning:

• Supervised learning: These types of models use labeled data to learn. Recommendation engines, regression, and classification are examples of supervised learning methods. The labels in these models can be user--movie ratings (for the recommendation), movie tags (in the case of the preceding classification example), or revenue figures (for regression). We will cover supervised learning models in Chapter 4, Building a Recommendation Engine with Spark, Chapter 6, Building a Classification Model with Spark, and Chapter 7, Building a Regression Model with Spark.
• Unsupervised learning: When a model does not require labeled data, we refer to unsupervised learning. These types of models try to learn or extract some underlying structure in the data or reduce the data down to its most important features. Clustering, dimensionality reduction, and some forms of feature extraction, such as text processing, are all unsupervised techniques and will be dealt with in Chapter 8, Building a Clustering Model with Spark, Chapter 9, Dimensionality Reduction with Spark, and Chapter 10, Advanced Text Processing with Spark.