This repository is created to keep track of Mathematics for Machine Learning provided by Coursera. This educational program is developed by Imperial College London and designed to getting 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.
- Eigenvalues And Eigenvectors
- Basis (Linear Algebra)
- Transformation Matrix
- Linear Algebra
- Linear Regression
- Vector Calculus
- Multivariable Calculus
- Gradient Descent
- Dimensionality Reduction
- Python Programming
- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA