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. OL24273942W Pages 142 Pdf_module_version 0.0.19 Ppi 300 Rcs_key 24143 Republisher_date 20200925173726 Republisher_operator Republisher_time 374 Scandate 20200922173005 Scanner Scanningcenter cebu Scribe3_search_catalog isbn Scribe3_search_id 9781926979052 Tts_version 4.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. Urn:lcp:iwritemathprecal0000appl:epub:479547ea-3b29-40b3-8700-fdc7ec75ccfb Foldoutcount 0 Identifier iwritemathprecal0000appl Identifier-ark ark:/13960/t9m42697w Invoice 1652 Isbn 9781926979038ġ926979044 Ocr tesseract 5.2.0-1-gc42a Ocr_detected_lang la Ocr_detected_lang_conf 1.0000 Ocr_detected_script Latin Ocr_detected_script_conf 0.8596 Ocr_module_version 0.0.17 Ocr_parameters -l eng Old_pallet IA19321 Openlibrary_edition Access-restricted-item true Addeddate 12:05:18 Associated-names Greg, Ranieri, author Boxid IA1943011 Camera Sony Alpha-A6300 (Control) Collection_set printdisabled External-identifier
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