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Before Machine Learning Volume 1 - Linear Algebra for A.I
Before Machine Learning Volume 1 - Linear Algebra for A.I

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

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Profile Icon Jorge Brasil
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€7.99
eBook May 2024 151 pages 1st Edition
eBook
€7.99
Arrow left icon
Profile Icon Jorge Brasil
Arrow right icon
€7.99
eBook May 2024 151 pages 1st Edition
eBook
€7.99
eBook
€7.99

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Key benefits

  • Comprehensive introduction to linear algebra for machine learning
  • Detailed exploration of vectors and matrices
  • In-depth study of principal component analysis (PCA)

Description

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.

Who is this book for?

This course is ideal for technical professionals, data scientists, aspiring machine learning engineers, and students of computer science or related fields. Additionally, it is beneficial for software developers, engineers, and IT professionals seeking to transition into data science or machine learning roles. A basic understanding of high school-level mathematics is recommended but not required, making it accessible for those looking to build a foundational understanding before diving into more advanced topics.

What you will learn

  • Understand the fundamental concepts of vectors and matrices
  • Implement principal component analysis in data reduction
  • Analyze the role of linear algebra in machine learning
  • Enhance problem-solving skills through practical applications
  • Gain the ability to interpret and manipulate high-dimensional data
  • Build confidence in using linear algebra for data science projects

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 24, 2024
Length: 151 pages
Edition : 1st
Language : English
ISBN-13 : 9781836208945
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : May 24, 2024
Length: 151 pages
Edition : 1st
Language : English
ISBN-13 : 9781836208945
Category :

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Table of Contents

6 Chapters
Chapter 1: We Start Here Chevron down icon Chevron up icon
Chapter 2: Why Linear Algebra? Chevron down icon Chevron up icon
Chapter 3: What Is a Vector? Chevron down icon Chevron up icon
Chapter 4: But What About a Matrix? Chevron down icon Chevron up icon
Chapter 5: Break Them Down - Matrix Decomposition Chevron down icon Chevron up icon
Chapter 6: The Final Stretch - Principal Component Analysis Chevron down icon Chevron up icon
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