In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. 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. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
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Imperial College London
Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
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- 3 stars3,37%
- 2 stars1,15%
- 1 star0,95%
Principais avaliações do MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA
Excellent course on the relevant parts of linear algebra for CS. Both instructors are great fun to watch and the assignments use up-to-date Python programming and Jupyter notebooks. Well done !!!
This is the BEST course if anyone wants to learn linear algebra for machine learning. Lectures are clear and very understandable and quiz questions are great, too. Thank you for this great course.
Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.
Excellent overview of Linear Algebra for those who had not formally taken up a course on the subject. I had taught myself linear algebra about 18 years back and this was a great refresher course
Sobre Programa de cursos integrados Matemática para aprendizagem automática
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
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