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

Start your machine learning journey by mastering linear algebra! This book explores essential concepts like vectors, matrices, and principal component analysis, forming the bedrock for data science and AI developments. With a mix of theoretical explanations and hands-on examples, you'll gain a deep understanding of how linear algebra underpins and enhances machine learning applications.

What this Book will help me do

• Master the fundamental techniques to work with vectors and matrices effectively.
• Understand how to use linear algebra for data transformations and machine learning tasks.
• Learn to perform principal component analysis for data dimensionality reduction.
• Develop the skills to analyze high-dimensional data and optimize computations.
• Build confidence in applying linear algebra knowledge to real-world AI scenarios.

Author(s)

Jorge Brasil is an experienced educator and author specializing in mathematical foundations for computer science and data analysis. With years of expertise in teaching linear algebra to budding data scientists, Jorge aims to make complex topics approachable and applicable. His clear, step-by-step method ensures readers achieve confidence and competence in understanding core concepts.

Who is it for?

This book is perfect for aspiring data scientists, ML engineers, and computer science students with an interest to delve into machine learning. It caters both to those with minimal mathematical background seeking a foundational understanding, as well as professionals aiming to reinforce their knowledge. If you want to understand the math behind AI, this is the book for you.