Practical Linear Algebra for Data Science in Python

Linear algebra is the mathematical engine powering modern machine learning, neural networks, and data science algorithms. To truly understand how models process data under the hood, you must master vectors, matrices, and their transformations. This text-based course bridges the gap between abstract mathematics and practical programming, helping you build a strong intuitive understanding of linear algebra concepts. Through clear written explanations and step-by-step code examples, you will learn how to translate mathematical theory directly into efficient, vectorized Python code. You will start with foundational definitions and key terminology before progressing to complex computational techniques used in modern AI pipelines. What you'll learn: - Understand vectors, matrices, and coordinate spaces from both mathematical and computational perspectives. - Solve complex systems of linear equations using NumPy and SciPy. - Apply key matrix decompositions, including LU, QR, and Singular Value Decomposition (SVD), to data problems. - Write clean, vectorized Python code using modern NumPy syntax and type hinting for mathematical operations. - Analyze vector spaces, eigenvalues, and eigenvectors to grasp the math behind dimensionality reduction. - Implement complex numbers and their operations within computational workflows. This course begins with core terminology, basic operations, and foundational definitions before moving into advanced matrix factorizations and practical applications. It is designed for beginner data scientists, programmers, and students who want to build a rock-solid mathematical foundation. No prior linear algebra experience is required, though a basic familiarity with Python is recommended. Start reading today to unlock the mathematical secrets behind modern machine learning algorithms.