This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We then start to build up a set of tools for making calculus easier and faster. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. 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. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set. Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. 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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