This chapter introduced the distinction between supervised and unsupervised learning. It covered how to use unsupervised learning (such as auto-encoders) to learn the deep or hidden features of data. These hidden features may be used on their own, such as to better understand the structure of data, or for other applications. Two common applications of auto-encoders and unsupervised learning are to identify anomalous data (for example, outlier detection, financial fraud) and to pre-train more complex, often supervised, models such as deep neural networks. In the next chapter, we will learn how to train and build deep neural networks to develop prediction models (that is, supervised learning).