Netflix is uses machine learning to give you personal recommendations of content to watch; Amazon uses machine learning to recommend products to buy based on what you've already bought. These are the so-called recommenders. They are usually (but not only) built using clustering techniques.

Machine learning techniques have been also used to diagnose illnesses. Aside from the application of clustering in cancer diagnosis already mentioned in Chapter 4, KDD, Data Mining, and Text Mining, neural networks can be trained to read various exams and even predict how likely a patient is to develop certain kinds of diseases—this field is called predictive medicine and highly benefits from machine learning advancements.

Saving endangered species is yet another wonderful usage of machine learning. Researchers from the University of Southern California Center for AI in Society have trained a neural network to detect illegal hunters that set foot in national parks from Zimbabwe and Malawi. This system is designed to distinguish hunters from animals using heat signatures and was baptized as Systematic POacher deTector (SPOT).

Tech workers, such as Zach Lubarsky and Ethan Phelps-Goodman, are actively engaging in data-driven campaigns to solve social issues. Lubarsky and Phelps-Goodman belong to the Seattle Tech 4 Housing organization, a community dedicated to improving Seattle's residence affordability.

A quick web search will tell you that there are many real-world applications of machine learning as there are stars in the sky. Talking about stars, how do you think that the galactical sized datasets generated by astronomers are being processed? That's right, machine learning.

This collection of methods can be separated into two classes: unsupervised (unlabeled) and supervised (labeled) learning. For the former, there is no target value to fit the models—hierarchical clusters are a good example of those. The objective of unsupervised learning is usually, but not always, to extract features from data rather than actual forecasts.

Supervised learning models are trained to target one or more variables; hence you need labeled data. Recurrent neural networks (RNNs) can be cited as a supervised learning technique. Although practical examples for both classes are provided in this chapter, more attention is given to unsupervised learning, since supervised is focused on in further chapters such as Chapter 8, Neural Networks and Deep Learning.

Although many concepts adopted in machine learning field are essentially the same as the ones that arose from traditional statisticians and forecasters, machine learning has a vocabulary of its own. Differences may have originated due to the main proponents of the field being more related to computing than statistics.

There is no downside to learning this vocabulary. A great way to do so is to relate machine learning terms to statistical ones. Moving on to the next section, we can see how many core ideas from machine learning can be somehow translated into statistical concepts.