Before Machine Learning Volume 1 – Linear Algebra

Has the abstract nature of linear algebra ever left you overwhelmed? Do you yearn to unlock the essence of machine learning but are bogged down by the intricacy of the mathematics? Dive into a realm where linear algebra unfolds not just as numerical operations, but as a powerful story. A story intertwined with the magic of machine learning, making sense of data, and unraveling algorithms that power tomorrow.

I am Jorge, a mathematician with over a decade of hands-on experience in data science and machine learning. Having navigated the intricate pathways of mathematical computations and machine learning algorithms myself, I wrote this book that differs itself from a traditional text book. With a conversational style and humour, I will guide through what you’ve been seeking on your journey into the depths of linear algebra.

Independently

This book isn’t just about understanding linear algebra—it’s about experiencing it. Dive into real-world applications, and grasp concepts that are foundational to machine learning:

Intuitive Understanding: Approach linear algebra as a story, where vectors and matrices come alive, making complex ideas feel intuitive and relatable.

Comprehensive Coverage: From the basics of vector addition and matrix multiplication to advanced topics like eigen decomposition and principal component analysis, get a 360-degree understanding.

Practical Applications: Discover how linear algebra powers algorithms, aiding in data interpretation and model building.

• # Linear Algebra (Books)