Linear Algebra and Learning from Data

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Publisher ‏ : ‎ Wellesley-Cambridge Press; First Edition (January 31, 2019)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 446 pages
ISBN-10 ‏ : ‎ 0692196382
ISBN-13 ‏ : ‎ 978-0692196380
Item Weight ‏ : ‎ 2.05 pounds
Dimensions ‏ : ‎ 7.72 x 0.98 x 9.53 inches

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Linear algebra is a fundamental branch of mathematics that plays a crucial role in the field of machine learning and data analysis. Understanding linear algebra is essential for anyone looking to work with data, as it provides the tools and concepts needed to manipulate and analyze large datasets.

In the context of machine learning, linear algebra is used to represent and solve complex mathematical models. From calculating matrix operations to performing eigenvalue decomposition, linear algebra techniques are at the core of many machine learning algorithms.

By studying linear algebra, data scientists can gain a deeper understanding of the underlying principles of data analysis and machine learning. Whether you are looking to build predictive models, perform dimensionality reduction, or solve optimization problems, a solid foundation in linear algebra is essential.

So, if you are looking to excel in the field of data science and machine learning, make sure to invest time in mastering the concepts of linear algebra. It will not only enhance your technical skills but also broaden your understanding of the mathematical principles that govern the world of data.
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