Summary

In this chapter, we learned about the main concepts in ML .

We discussed different definitions and subdomains of artificial intelligence, including ML . ML is the science and practice of extracting knowledge from data. We also explained the motivation behind ML . We had a brief overview of its application domains: digital signal processing, computer vision, and natural language processing.

We learned about the two core concepts in ML : the data, and the model. Your model is only as good as your data. A typical ML dataset consists of samples; each sample consists of features. There are many types of features and many techniques to extract useful information from the features. These techniques are known as feature engineering. For supervised learning tasks, dataset also includes label for each of the samples. We provided an overview of data collection and preprocessing.

Finally, we learned about three types of common ML tasks: supervised, unsupervised, and reinforcement learning. In the next chapter, we're going to build our first ML application.