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Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence

Język publikacji: 1
Autor:
Jorge Brasil
Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence Jorge Brasil - okladka książki

Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence Jorge Brasil - okladka książki

Serie wydawnicze:
Learning
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
151
Dostępne formaty:
     PDF
     ePub

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39,90 zł

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In this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field.

As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance.

This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.

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O autorze książki

Jorge Brasil is a mathematician with over a decade of experience in developing and deploying machine learning systems. His books are expertly written in a conversational style with a touch of humor. They are rich in practical applications, enabling readers to not only grasp the concepts but also understand their real-world applications in the field of Data Science.

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