Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.Īt the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. 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. 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. In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. This course is of intermediate difficulty and will require Python and numpy knowledge.Īt the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. Then we look through what vectors and matrices are and how to work with them. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. 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. 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.
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