Price: $64.99 - $61.18
(as of Nov 24,2024 19:24:14 UTC – Details)

Publisher ‏ : ‎ Cambridge University Press; 1st edition (July 11, 2024)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 450 pages
ISBN-10 ‏ : ‎ 1009418149
ISBN-13 ‏ : ‎ 978-1009418140
Item Weight ‏ : ‎ 2.05 pounds
Dimensions ‏ : ‎ 6.8 x 1.2 x 9.7 inches

Linear Algebra is a fundamental mathematical concept that plays a crucial role in various fields such as Data Science, Machine Learning, and Signal Processing. In these fields, linear algebra is used to represent and manipulate data, solve complex problems, and make predictions.

In Data Science, linear algebra is used for tasks such as data preprocessing, dimensionality reduction, and regression analysis. By representing data as matrices and vectors, analysts can efficiently perform operations such as matrix multiplication, matrix inversion, and eigenvalue decomposition to extract meaningful insights from the data.

In Machine Learning, linear algebra is essential for building and training models. Techniques such as linear regression, support vector machines, and neural networks rely heavily on linear algebra operations to optimize model parameters, make predictions, and evaluate model performance.

In Signal Processing, linear algebra is used to analyze and manipulate signals. By representing signals as vectors and matrices, engineers can perform operations such as filtering, compression, and noise reduction to extract useful information and enhance signal quality.

Overall, a solid understanding of linear algebra is essential for professionals working in the fields of Data Science, Machine Learning, and Signal Processing. By mastering key concepts such as matrix operations, eigenvalues, and eigenvectors, practitioners can effectively analyze data, build predictive models, and process signals to drive innovation and make informed decisions.
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