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. 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. Key takeaways include: Mastering vectors and matrices in real-world scenarios. The magic behind eigenvectors, eigenvalues, and their applications. Gaining insights into advanced topics like the singular value decomposition. And this is just the tip of the iceberg. Dive in to uncover the essence of machine learning through the lens of linear algebra, and let mathematics weave its story. Ready to embark on this transformative journey? Don't miss out—let the power of linear algebra unveil the mysteries of machine learning. 24/01/2024 - UPDATES Refined definition of vectors spaces. New example of the dot product. Notation fixes. Complementary exercises. Grab your copy now!