Linear Algebra for Machine Learning and Computer Graphics

Modern technologies like artificial intelligence, neural networks, and 3D rendering engines rely heavily on the mathematics of vector spaces and matrix transformations. Understanding these core mathematical concepts is essential for writing efficient code and debugging complex algorithms in these fields. This text-based course bridges the gap between abstract academic theory and practical software implementation. You will start with the absolute fundamentals of vectors and matrices, gradually building up to complex topics like eigenvalues, matrix calculus, and dimensionality reduction, all illustrated with clean Python code snippets. What you'll learn: - Understand core vector and matrix operations, including multiplication, determinants, and inverses. - Apply linear transformations to manipulate 2D and 3D objects in computer graphics. - Master matrix calculus and gradient descent mechanics used to train deep learning models. - Implement vectorized operations in Python using modern numerical computing practices. - Analyze high-dimensional datasets using principal component analysis and singular value decomposition. - Solve systems of linear equations using Gaussian elimination and matrix factorization methods. The course begins with foundational definitions and basic geometric interpretations before advancing to algebraic proofs and programmatic applications. Through structured written explanations and step-by-step code walkthroughs, you will develop a deep intuitive grasp of the mathematics driving modern technology. This course is designed for aspiring data scientists, game developers, and software engineers who want to learn the mathematical foundations of their field from scratch, with no prior advanced math required. Start reading today to unlock the mathematical principles behind modern algorithms.