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Learn Linear Algebra, earn certificates with paid and free online courses from Harvard, Stanford, MIT, University of Michigan and other top universities around the world. Read reviews to decide if a class is right for you.
This course is all about matrices, and concisely covers the linear algebra that an engineer should know. We define matrices and how to add and multiply them, and introduce some special types of matrices.
Learn to use R programming to apply linear models to analyze data in life sciences.
Learn the mathematics behind linear algebra and link it to matrix software development.
This course is about differential equations and covers material that all engineers should know. Both basic theory and applications are taught.
Explore linear algebra fundamentals and their applications in machine learning, covering vectors, matrices, eigenvalues, and practical implementations in Python for data-driven tasks.
This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering.
This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.
Master essential linear algebra concepts and operations needed for machine learning, from vectors and matrices to determinants and linear dependency, with hands-on Python implementation.
Master essential vector and matrix operations in NumPy, from basic arithmetic to advanced techniques like matrix inversion, while building practical linear algebra skills for data manipulation.
Master advanced linear algebra concepts using NumPy, from eigenvalues and eigenvectors to matrix diagonalization and SVD, with hands-on applications in Python.
Explore linear systems, matrices, and vector equations, gaining foundational skills in linear algebra. Learn to solve and classify linear systems, understand linear transformations, and apply concepts to real-world problems.
Develop matrix algebra techniques, study determinants, and explore eigenvectors to analyze linear transformations. Apply concepts to Markov chains and Google PageRank algorithm.
Explore orthogonality, diagonalization, and symmetric matrices, gaining skills applicable to AI and machine learning. Develop a strong foundation for advanced studies in data science and mathematics.
Comprehensive introduction to linear algebra, covering vectors, matrices, and their applications. Emphasizes both algebraic and geometric understanding, preparing students for advanced topics in various fields.
Professor Strang presents a fresh approach to linear algebra, focusing on matrices. This concise series covers key concepts like column space, orthogonality, eigenvalues, and solving linear systems.
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