Applied Linear Algebra for Data Science & Machine Learning

Many powerful techniques in data science, machine learning, and signal processing rely on a solid grasp of linear algebra. Without this foundational knowledge, understanding advanced algorithms can be challenging. This course provides a clear, text-based introduction to the essential linear algebra concepts you need to confidently approach and implement these modern applications. You will develop a robust understanding of how linear algebra underpins key analytical and computational methods. What you'll learn: Understand fundamental concepts of vectors, matrices, and tensors. Apply core linear algebra operations like matrix multiplication, inversion, and determinants. Master essential concepts such as eigenvalues, eigenvectors, and singular value decomposition (SVD). Learn how linear transformations are used in data dimensionality reduction and feature engineering. Explore the linear algebra foundations of machine learning algorithms, including neural networks and principal component analysis. Practice applying linear algebra techniques to basic signal processing problems. Starting with basic definitions and operations, the course progressively builds towards more complex topics, demonstrating their practical relevance across various computational domains. Each section includes written explanations and practice exercises to reinforce your learning. This course is designed for absolute beginners with no prior knowledge of linear algebra. No prerequisites are required to start learning. Start your journey to mastering the linear algebra that powers today's data-driven world.