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Machine learning for extra-terrestrial life explorers
Swift is undoubtedly the programming language of the future. In the nearest years, we're expecting to see Swift being employed to program-intelligent scout robots that will explore alien planets and life forms on them. These robots should be able to recognize and classify aliens they will encounter. Let's build a model to distinguish between two alien species using their characteristic features.
The biosphere of the distant planet consists mainly of two species: night predators rabbosauruses, and peaceful, herbivorous platyhogs (see the following diagram). Roboscouts are equipped with sensors to measure only three features of each individual: length (in meters), color, and fluffiness.
![](https://epubservercos.yuewen.com/A3C592/19470396901583706/epubprivate/OEBPS/Images/9e718727-51ff-4520-89a4-0b6283ca2507.png?sign=1739288214-XMYmiH1YSJgmaEFFywm1OHyec9VhrJhu-0-1095b86e4953a7a27c9418300f9f68eb)
Figure 2.1: Objects of interest in our first machine learning task. Picture by Mykola Sosnovshchenko.
The full code of the Python part of this chapter can be found here: ML_Intro.ipynb .