Applied Vector Algebra for Machine Learning and Computer Graphics

Many aspiring developers and data scientists struggle with machine learning and computer graphics because they lack a solid grasp of the underlying mathematics. This text-based course bridges that gap by demystifying the essential vector algebra and analytical geometry that power modern algorithms. Through clear, written explanations and step-by-step mathematical breakdowns, you will transition from basic coordinate systems to advanced spatial transformations. You will understand exactly how algorithms compute similarity, rotate 3D objects, and process high-dimensional data, giving you the confidence to write better code and design smarter systems. What you'll learn: - Understand core vector operations, coordinate spaces, and geometric principles from the ground up - Apply dot products and cosine similarity to machine learning models and modern vector search databases - Calculate 3D transformations, projections, and rotations used in computer graphics and physics engines - Solve spatial problems using analytical geometry concepts like planes, lines, and intersections - Analyze how high-dimensional vectors represent complex data embeddings in modern AI applications The course begins with fundamental definitions of vectors and matrices, ensuring you build a strong theoretical foundation. You will then progress through practical written exercises that demonstrate how these mathematical concepts directly translate into programming logic for data science and graphics. This course is designed for beginners, self-taught programmers, and aspiring data analysts who want to build a strong mathematical foundation without needing prior advanced university-level math. Start reading today to unlock the mathematical foundations of modern technology.