## Mathematics for Machine Learning Specialization

Start Date: 07/12/2020

 Course Type: Specialization Course

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. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At 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.

#### Course Introduction

Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning Mathematics for Machine Learning Specialization Mathematics for Machine Learning is a cross-disciplinary specialisation designed to educate undergraduates in Mathematics and computer science on fundamental topics in Machine Learning, including: *Mathematical modeling and algorithms, including basic level of differentiation, kernel methods, and numerical methods *Mathematical modeling and algorithms, including basic level of differentiation, Kmeans, and numerical methods *Mathematical modeling and algorithms, including basic level of differentiation, Naive Bayes, and random initialization techniques Mathematical modeling and algorithms, including basic level of differentiation, linearized dilatonics, and random initialization techniques Mathematical modeling and algorithms, including basic level of differentiation, random differentiation, and random differentiation Mathematical modeling and algorithms, including basic level of differentiation, and linearized dilatonics Mathematical modeling and algorithms, including basic level of differentiation, and random differentiation Mathematical modeling and algorithms, including basic level of differentiation, and random differentiation Mathematical simulation and optimization, and random variables. Recommended background: To ensure that you have the required background information, we have prepared a compatibility list for Python programs. Check it out: https://raw.githubusercontent.com/TensorFlow/master/comparison/list Summary of course: After completing this course, you will: * Understand how Machine Learning is used in the industry

#### Course Tag

PCA Multivariate Calculus Mathematics for Machine Learning Mathematics Machine Learning Linear Algebra Data Science Eigenvalues And Eigenvectors Principal Component Analysis (PCA) Multivariable Calculus

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