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6.0: Introduction

Principles of Data Science · LibreTexts

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    From LibreTexts, under CC BY 4.0, as the source states it; its equations are written in TeX, which the PDF shows typeset. This page at LibreTexts.

    Person at a microphone “talking” to a humanoid robot at a trade show.
    Figure 6.1 Machine learning may be used to classify all kinds of data. Facial recognition technology relies heavily on machine learning algorithms, such as random forest models. (credit: modification of work "Humanoid Robot Uprising" by Steve Jurvetson/Flickr, CC BY 2.0)

    Imagine a world in which your face is your passport. No need for keys, cards, or passwords; your identity is simply your unique appearance. In this world, machine learning plays the role of gatekeeper, using the visible features of your face to identify you and grant you access. Machine learning has a broad range of applications that go beyond facial recognition, transforming the way we interact with technology. As we’ll see in this chapter and Deep Learning and AI Basics, machine learning is also used in medical image analysis, satellite image interpretation, and augmented reality (e.g., in gaming, education/training, and architecture/design). It is used to power recommendation systems and for predictive analytics. In this chapter, we explore the fundamentals of classification, clustering, and regression algorithms that allow machines to learn how to label data accurately and make useful decisions that can be put to use in these many applications.

    This page titled 6.0: Introduction was last modified on Tue, 22 Apr 2025 14:17:53 GMT and is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by OpenStax via source content that was edited to the style and standards of the LibreTexts platform.