Before Machine Learning Volume 1 - Linear Algebra

Book Synopsis: Why: Linear algebra is a fundamental topic for anyone working in machine learning, and it plays a critical role in understanding the inner workings of algorithms and data models. In this book, you’ll learn how to apply linear algebra to real-world problems and gain a deep understanding of the concepts that drive machine learning.
What is different: What sets this book apart is its different approach to teaching. Rather than presenting abstract mathematical concepts in isolation, the content is structured like a story with real-life examples that illustrate the practical applications of linear algebra. It is written in a conversational style as if you were having a one-on-one conversation with me, and the structure resembles a story.
To whom: Whether you’re a beginner or an experienced practitioner, this book will help you master the essentials of linear algebra and build a solid foundation for your machine-learning journey. It assumes no prior knowledge of linear algebra, making it perfect for beginners. However, it also includes advanced concepts, making it a valuable resource for more experienced learners.
What's inside: This book covers all the essential topics in linear algebra, from vectors and matrices to eigenvalues and eigenvectors. It also includes in-depth discussions of applications of linear algebra, such as principal component analysis, and single-value decomposition.
Vectors addition.
Multiplication of a vector by a scalar.
The dot product.
Vectors spaces, linear combinations, linear independence, and basis.
Change of basis.
Matrix and vector multiplication as well as Matrix matrix multiplication.
Outer products.
The inverse of a matrix.
The Determinante.
Systems of linear equations.
Eigenvectors and eigenvalues.
Eigen decomposition.
The single value decomposition.
The principal component analysis.