Mathematics for Machine Learning (Offered by Imperial College London)
Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning.
What you will learn:
- Implement mathematical concepts using real-world data
- Derive PCA from a projection perspective
- Understand how orthogonal projections work
- Master PCA
Skills you will gain:
Eigenvalues And Eigenvectors, Principal Component Analysis(PCA), Multivariable Calculus, Linear Algebra, Basis (Linear Algebra), Transformation Matrix, Linear Regression, Vector Calculus , Gradient Descent, Dimensionality Reduction, Python Programming.
Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.
***There are 3 Courses in this Specialization***
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices.
This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques.
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique.
About this 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.
Applied Learning Project:
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. 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.
Offered by Imperial College London.
Professor of Metallurgy, Department of Materials
Samuel J. Cooper
Senior Lecturer, Dyson School of Design Engineering
A. Freddie Page
Strategic Teaching Fellow, Dyson School of Design Engineering
Marc Peter Deisenroth
Lecturer in Statistical Machine Learning, Department of Computing