Mathematics for Machine Learning

Completed by DANIEL DE SOUSA ROMERO

November 4, 2021

Approximately 15 weeks to complete.

Course Certificates Completed

Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Multivariate Calculus

Mathematics for Machine Learning: PCA

View certificate for DANIEL DE SOUSA ROMERO, Mathematics for Machine Learning, offered through Coursera. A sequence of 3 courses on the prerequisite mathematics for applications in data science and machine learning.  
Successful participants learn how to represent data in a linear algebra context and manipulate these objects mathematically. They are able to summarise properties of data sets and map them onto lower dimensional spaces with principal component analysis. Finally they can solve optimisation problems and use this skill to train models for describing data such as simple neural networks.

Course Certificates

Earned after completing each course in the Specialization

Mathematics for Machine Learning: Linear Algebra

Imperial College London

Taught by: David Dye, Samuel J. Cooper & A. Freddie Page

Completed by: DANIEL DE SOUSA ROMERO by October 31, 2021

5 weeks of study, 2-5 hours/week

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Mathematics for Machine Learning: Multivariate Calculus

Imperial College London

Taught by: Samuel J. Cooper, David Dye & A. Freddie Page

Completed by: DANIEL DE SOUSA ROMERO by November 4, 2021

6 weeks of study, 2-5 hours/week

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Mathematics for Machine Learning: PCA

Imperial College London

Taught by: Marc Peter Deisenroth

Completed by: DANIEL DE SOUSA ROMERO by November 4, 2021

4 weeks of study, 4-6 hours/week

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