Really enjoyed the course! Don't expect you will dive deep inside the Linear Algebra. The course is very good, almost perfect for my purposes. Some videos on Youtube are visually more capturing than blackboard style teaching here. Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus Great way to learn about applied Linear Algebra. This course is completely online, so thereâs no need to show up to a classroom in person. Some lectures were packed with examples, and some had none at all. I will update this post when I decide where I will be going next. ... GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. If you cannot afford the fee, you can apply for financial aid. The lecture style is same as machine learning course. The programming assignment do require previous Python/other programming experience. If you call a course Math for Machine Learning, I would expect that you relate the concepts to Machine Learning. I guess this is an important concept but I was not sure how this relates to machine learning. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. But please don't give up and push it through to completion. Excellent course. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. Too many derivatives of pointless functions. Basis, vector space, and linear independence, Composition or combination of matrix transformations, Solving the apples and bananas problem: Gaussian elimination, Going from Gaussian elimination to finding the inverse matrix, Solving linear equations using the inverse matrix, Doing a transformation in a changed basis, Example: Using non-square matrices to do a projection, Characteristic polynomials, eigenvalues and eigenvectors, Mathematics for Machine Learning Specialization, MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA, About the Mathematics for Machine Learning Specialization. The content is abundant,i really love the visualization and programming work.The programming work is fascinating,elabrated-designed,fully explained,i want more and harder programming work. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems.