About the 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.
Skills you will gain
- Eigenvalues And Eigenvectors
- Principal Component Analysis (PCA)
- Multivariable Calculus
- Linear AlgebraBasis (Linear Algebra)
- Transformation Matrix
- Linear Regression
- Vector Calculus
- Gradient Descent
- Dimensionality Reduction
- Python Programming
There are 3 Courses in this Specialization
- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA
Course on Mathematics for Machine Learning
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