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In Practical Linear Algebra for Data Science, Mike X Cohen delves into the essential mathematical concepts that underpin data science, making them accessible and applicable for practitioners. This book serves as a bridge between theoretical linear algebra and its practical applications within the realm of machine learning.
Cohen's exploration begins with foundational concepts, gently guiding readers through the intricacies of vectors, matrices, and transformations. Each chapter builds upon the last, culminating in powerful techniques used in data analysis and machine learning algorithms. With a focus on real-world examples, the text illustrates how linear algebra is used to make sense of complex datasets, enabling readers to develop a robust understanding of data manipulation and interpretation.
This book is ideal for anyone looking to deepen their understanding of linear algebra in the context of data science, including:
"Cohen’s work is an invaluable resource for those navigating the dynamic world of data science." - A Reader
Format: Paperback / softback
Dimensions: × ×
Pages: 300
Publisher: O'Reilly Media
ISBN: 9781098120610
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